Analysis, Assessment, Rebuild

Bahmni and Legacy Code Migration

Bahmni is an open-source Hospital Administration
System & EMR constructed to help healthcare supply in low-resource
settings offering a wealthy interface for medical and administrative customers.
The Bahmni frontend was initially
developed utilizing AngularJS (model 1.x)—an
early however highly effective framework for constructing dynamic net purposes.
Nevertheless, AngularJS has lengthy been deprecated by the Angular crew at Google,
with official long-term help having resulted in December 2021.

Regardless of this, Bahmni continues to rely closely on AngularJS for a lot of of
its core workflows. This reliance introduces important dangers, together with
safety vulnerabilities from unpatched dependencies, problem in
onboarding builders unfamiliar with the outdated framework, restricted
compatibility with trendy instruments and libraries, and diminished maintainability
as new necessities are constructed on an ageing codebase.

In healthcare programs, the continued use of outdated software program can
adversely have an effect on medical workflows and compromise affected person information security.
For Bahmni, frontend migration has change into a essential precedence.

Analysis, Assessment, Rebuild

Determine 1: Analysis, Assessment, Rebuild Workflow

The workflow I adopted known as “Analysis, Assessment, Rebuild” — the place
we do a function migration analysis utilizing a few MCP servers, validate
and approve the method AI proposes, rebuild the function after which as soon as
all of the code era is finished, refactor issues that you simply did not like.

The Workflow

1. Put together a listing of options focused for migration. Choose one function to
   start with.
2. Use Mannequin Context Protocol (MCP) servers to analysis the chosen function
   by producing a contextual evaluation of the chosen function via a Massive
   Language Mannequin (LLM).
3. Have area consultants evaluation the generated evaluation, making certain it’s
   correct, aligns with current challenge conventions and architectural pointers.
   If the function will not be sufficiently remoted for migration, defer it and replace
   the function record accordingly.
4. Proceed with LLM-assisted rebuild of the validated function to the goal
   system or framework.
5. Till the record is empty, return to #2

Earlier than Getting Began

Earlier than we proceed with the workflow, it’s important to have a
high-level understanding of the present codebase and decide which
elements must be retained, discarded, or deferred for future
consideration.

Within the context of Bahmni, Show
Controls
are modular, configurable widgets that may be embedded throughout varied
pages to boost the system’s flexibility. Their decoupled nature makes
them well-suited for focused modernization efforts. Bahmni at the moment
consists of over 30 show controls developed over time. These controls are
extremely configurable, permitting healthcare suppliers to tailor the interface
to show pertinent information like diagnoses, therapies, lab outcomes, and
extra. By leveraging show controls, Bahmni facilitates a customizable
and streamlined person expertise, aligning with the various wants of
healthcare settings.

All the present Bahmni show controls are constructed over OpenMRS REST
endpoint, which is tightly coupled with the OpenMRS information mannequin and
particular implementation logic. OpenMRS (Open
Medical Report System) is an open-source platform designed to function a
foundational EMR system primarily for low-resource environments offering
customizable and scalable methods to handle well being information, particularly in
creating nations. Bahmni is constructed on prime of OpenMRS, counting on
OpenMRS for medical information modeling and affected person file administration, utilizing
its APIs and information constructions. When somebody makes use of Bahmni, they’re
primarily utilizing OpenMRS as half of a bigger system.

FHIR (Quick Healthcare
Interoperability Sources) is a contemporary commonplace for healthcare information
alternate, designed to simplify interoperability by utilizing a versatile,
modular method to symbolize and share medical, administrative, and
monetary information throughout programs. FHIR was launched by
HL7 (Well being Degree Seven Worldwide), a
not-for-profit requirements improvement group that performs a pivotal
position within the healthcare trade by creating frameworks and requirements for
the alternate, integration, sharing, and retrieval of digital well being
info. The time period “Well being Degree Seven” refers back to the seventh layer
of the OSI (Open Programs
Interconnection) mannequin—the applying
layer,
chargeable for managing information alternate between distributed programs.

Though FHIR was initiated in 2011, it reached a big milestone
in December 2018 with the discharge of FHIR Launch 4 (R4). This launch
launched the primary normative content material, marking FHIR’s evolution right into a
secure, production-ready commonplace appropriate for widespread adoption.

Bahmni’s improvement commenced in early 2013, throughout a time when FHIR
was nonetheless in its early levels and had not but achieved normative standing.
As such, Bahmni relied closely on the mature and production-proven OpenMRS
REST API. Given Bahmni’s dependence on OpenMRS, the supply of FHIR
help in Bahmni was inherently tied to OpenMRS’s adoption of FHIR. Till
lately, FHIR help in OpenMRS remained restricted, experimental, and
lacked complete protection for a lot of important useful resource varieties.

With the latest developments in FHIR help inside OpenMRS, a key
precedence within the ongoing migration effort is to architect the goal system
utilizing FHIR R4. Leveraging FHIR endpoints facilitates standardization,
enhances interoperability, and simplifies integration with exterior
programs, aligning the system with globally acknowledged healthcare information
alternate requirements.

For the aim of this experiment, we’ll focus particularly on the
Therapies Show Management as a consultant candidate for
migration.

Determine 2: Legacy Therapies Show Management constructed utilizing
Angular and built-in with OpenMRS REST endpoints

The Remedy Particulars Management is a particular kind of show management
in Bahmni that focuses on presenting complete details about a
affected person’s prescriptions or drug orders over a configurable variety of
visits. This management is instrumental in offering clinicians with a
consolidated view of a affected person’s remedy historical past, aiding in knowledgeable
decision-making. It retrieves information by way of a REST API, processing it right into a
view mannequin for UI rendering in a tabular format, supporting each present
and historic therapies. The management incorporates error dealing with, empty
state administration, and efficiency optimizations to make sure a strong and
environment friendly person expertise.

The information for this management is sourced from the
/openmrs/ws/relaxation/v1/bahmnicore/drugOrders/prescribedAndActive endpoint,
which returns visitDrugOrders. The visitDrugOrders array incorporates
detailed entries that hyperlink drug orders to particular visits, together with
metadata concerning the supplier, drug idea, and dosing directions. Every
drug order consists of prescription particulars comparable to drug title, dosage,
frequency, length, administration route, begin and cease dates, and
commonplace code mappings (e.g., WHOATC, CIEL, SNOMED-CT, RxNORM).

Here’s a pattern JSON response from Bahmni’s
/bahmnicore/drugOrders/prescribedAndActive REST API endpoint containing
detailed details about a affected person’s drug orders throughout a particular
go to, together with metadata like drug title, dosage, frequency, length,
route, and prescribing supplier.

{
  "visitDrugOrders": [
    {
      "visit": {
        "uuid": "3145cef3-abfa-4287-889d-c61154428429",
        "startDateTime": 1750033721000
      },
      "drugOrder": {
        "concept": {
          "uuid": "70116AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA",
          "name": "Acetaminophen",
          "dataType": "N/A",
          "shortName": "Acetaminophen",
          "units": null,
          "conceptClass": "Drug",
          "hiNormal": null,
          "lowNormal": null,
          "set": false,
          "mappings": [
            {
              "code": "70116",
              "name": null,
              "source": "CIEL"
            },y
            /* Response Truncated */
          ]
        },
        "directions": null,
        "uuid": "a8a2e7d6-50cf-4e3e-8693-98ff212eee1b",

        "orderType": "Drug Order",
        "accessionNumber": null,
        "orderGroup": null,
        "dateCreated": null,
        "dateChanged": null,
        "dateStopped": null,
        "orderNumber": "ORD-1",
        "careSetting": "OUTPATIENT",
        "motion": "NEW",
        "commentToFulfiller": null,
        "autoExpireDate": 1750206569000,
        "urgency": null,
        "previousOrderUuid": null,
        "drug": {
          "title": "Paracetamol 500 mg",
          "uuid": "e8265115-66d3-459c-852e-b9963b2e38eb",
          "type": "Pill",
          "energy": "500 mg"
        },
        "drugNonCoded": null,
        "dosingInstructionType": "org.openmrs.module.bahmniemrapi.drugorder.dosinginstructions.FlexibleDosingInstructions",
        "dosingInstructions": {
          "dose": 1.0,
          "doseUnits": "Pill",
          "route": "Oral",
          "frequency": "Twice a day",
          "asNeeded": false,
          "administrationInstructions": "{"directions":"As directed"}",
          "amount": 4.0,
          "quantityUnits": "Pill",
          "numberOfRefills": null
        },
        "dateActivated": 1750033770000,
        "scheduledDate": 1750033770000,
        "effectiveStartDate": 1750033770000,
        "effectiveStopDate": 1750206569000,
        "orderReasonText": null,
        "length": 2,
        "durationUnits": "Days",
        "voided": false,
        "voidReason": null,
        "orderReasonConcept": null,
        "sortWeight": null,
        "conceptUuid": "70116AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
      },
      "supplier": {
        "uuid": "d7a67c17-5e07-11ef-8f7c-0242ac120002",
        "title": "Tremendous Man",
        "encounterRoleUuid": null
      },
      "orderAttributes": null,
      "retired": false,
      "encounterUuid": "fe91544a-4b6b-4bb0-88de-2f9669f86a25",
      "creatorName": "Tremendous Man",
      "orderReasonConcept": null,
      "orderReasonText": null,
      "dosingInstructionType": "org.openmrs.module.bahmniemrapi.drugorder.dosinginstructions.FlexibleDosingInstructions",
      "previousOrderUuid": null,
      "idea": {
        "uuid": "70116AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA",
        "title": "Acetaminophen",
        "dataType": "N/A",
        "shortName": "Acetaminophen",
        "items": null,
        "conceptClass": "Drug",
        "hiNormal": null,
        "lowNormal": null,
        "set": false,
        "mappings": [
          {
            "code": "70116",
            "name": null,
            "source": "CIEL"
          },
          /* Response Truncated */
        ]
      },
      "sortWeight": null,
      "uuid": "a8a2e7d6-50cf-4e3e-8693-98ff212eee1b",
      "effectiveStartDate": 1750033770000,
      "effectiveStopDate": 1750206569000,
      "orderGroup": null,
      "autoExpireDate": 1750206569000,
      "scheduledDate": 1750033770000,
      "dateStopped": null,
      "directions": null,
      "dateActivated": 1750033770000,
      "commentToFulfiller": null,
      "orderNumber": "ORD-1",
      "careSetting": "OUTPATIENT",
      "orderType": "Drug Order",
      "drug": {
        "title": "Paracetamol 500 mg",
        "uuid": "e8265115-66d3-459c-852e-b9963b2e38eb",
        "type": "Pill",
        "energy": "500 mg"
      },
      "dosingInstructions": {
        "dose": 1.0,
        "doseUnits": "Pill",
        "route": "Oral",
        "frequency": "Twice a day",
        "asNeeded": false,
        "administrationInstructions": "{"directions":"As directed"}",
        "amount": 4.0,
        "quantityUnits": "Pill",
        "numberOfRefills": null
      },
      "durationUnits": "Days",
      "drugNonCoded": null,
      "motion": "NEW",
      "length": 2
    }
  ]
}

The /bahmnicore/drugOrders/prescribedAndActive mannequin differs considerably
from the OpenMRS FHIR
MedicationRequest
mannequin in each construction and illustration. Whereas the Bahmni REST mannequin is
tailor-made for UI rendering with visit-context grouping and consists of
OpenMRS-specific constructs like idea, drug, orderNumber, and versatile
dosing directions, the FHIR MedicationRequest mannequin adheres to worldwide
requirements with a normalized, reference-based construction utilizing assets comparable to
Remedy, Encounter, Practitioner, and coded parts in
CodeableConcept and Timing.

Analysis

The “Analysis” section of the method includes producing an
MCP-augmented LLM evaluation of the chosen Show Management. This section is
centered round understanding the legacy system’s habits by inspecting
its supply code and conducting reverse engineering. Such evaluation is
important for informing the ahead engineering efforts. Whereas not all
recognized necessities could also be carried ahead—notably in long-lived
programs the place sure functionalities might have change into out of date—it’s
essential to have a transparent understanding of current behaviors. This permits
groups to make knowledgeable selections about which parts to retain, discard,
or redesign within the goal system, making certain that the modernization effort
aligns with present enterprise wants and technical targets.

At this stage, it’s useful to take a step again and take into account how human
builders usually method a migration of this nature. One key perception
is that migrating from Angular to React depends closely on contextual
understanding. Builders should draw upon varied dimensions of information
to make sure a profitable and significant transition. The essential areas of
focus usually embrace:

• Goal Analysis: understanding the practical intent and position of the
  current Angular elements inside the broader utility.
• Knowledge Mannequin Evaluation: reviewing the underlying information constructions and their
  relationships to evaluate compatibility with the brand new structure.
• Knowledge Movement Mapping: tracing how information strikes from backend APIs to the
  frontend UI to make sure continuity within the person expertise.
• FHIR Mannequin Alignment: figuring out whether or not the present information mannequin could be
  mapped to an HL7 FHIR-compatible construction, the place relevant.
• Comparative Evaluation: evaluating structural and practical similarities,
  variations, and potential gaps between the previous and goal implementations.
• Efficiency Issues: considering areas for efficiency
  enhancement within the new system.
• Function Relevance: assessing which options must be carried ahead,
  redesigned, or deprecated based mostly on present enterprise wants.

This context-driven evaluation is usually probably the most difficult side of
any legacy migration. Importantly, modernization will not be merely about
changing outdated applied sciences—it’s about reimagining the way forward for the
system and the enterprise it helps. It includes the evolution of the
utility throughout its complete lifecycle, together with its structure, information
constructions, and person expertise.

The experience of material consultants (SMEs) and area specialists
is essential to grasp current habits and to arrange a information for the
migration. And what higher solution to seize the anticipated habits than
via well-defined take a look at eventualities in opposition to which the migrated code will
be evaluated. Understanding what eventualities are to be examined is essential
not simply in ensuring that – every thing that used to work nonetheless works
and the brand new habits would work as anticipated but additionally as a result of now your LLM
has a clearly outlined set of targets that it is aware of is what’s anticipated. By
defining these targets explicitly, we are able to make the LLM’s responses as
deterministic as attainable, avoiding the unpredictability of probabilistic
responses and making certain extra dependable outcomes in the course of the migration
course of.

Based mostly on this understanding, I developed a complete and
strategically structured immediate
designed to seize all related info successfully.

Whereas the immediate covers all anticipated areas—comparable to information movement,
configuration, key features, and integration—it additionally consists of a number of
sections that warrant particular point out:

• FHIR Compatibility: this part maps the customized Bahmni information mannequin
  to HL7 FHIR assets and highlights gaps, thereby supporting future
  interoperability efforts. Finishing this mapping requires a stable understanding
  of FHIR ideas and useful resource constructions, and is usually a time-consuming job. It
  usually includes a number of hours of detailed evaluation to make sure correct
  alignment, compatibility verification, and identification of divergences between
  the OpenMRS and FHIR remedy fashions, which might now be carried out in a matter of
  seconds.
• Testing Tips for React + TypeScript Implementation Over OpenMRS
  FHIR: this part gives structured take a look at eventualities that emphasize information
  dealing with, rendering accuracy, and FHIR compliance for the modernized frontend
  elements. It serves as a superb basis for the event course of,
  setting out a compulsory set of standards that the LLM ought to fulfill whereas
  rebuilding the element.
• Customization Choices: this outlines accessible extension factors and
  configuration mechanisms that improve maintainability and flexibility throughout
  various implementation eventualities. Whereas a few of these choices are documented,
  the LLM-generated evaluation typically uncovers further customization paths
  embedded within the codebase. This helps establish legacy customization approaches
  extra successfully and ensures a extra exhaustive understanding of present
  capabilities.

To assemble the required information, I utilized two light-weight servers:

• An Atlassian MCP server to extract any accessible documentation on the
  show management.
• A filesystem MCP server, the place the legacy frontend code and configuration
  have been mounted, to offer supply code-level evaluation.

Determine 3: MCP + Cline + Claude Setup Diagram

Whereas optionally available, this filesystem server allowed me to deal with the goal
system’s code inside my IDE, with the legacy reference codebases conveniently
accessible via the mounted server.

These gentle weight servers every expose particular capabilities via the
standardized Mannequin Context Protocol, which is then utilized by Cline (my shopper in
this case) to entry the code base, documentation and configuration. For the reason that
configurations shipped are opinionated and the paperwork typically outdated, I added
particular directions to take the supply code as the only supply of reality and
the remaining as a supplementary reference.

Assessment

The second section of the method —is the place the human within the loop
turns into invaluable.

The AI-generated evaluation is not meant to be accepted at face worth,
particularly for advanced codebases. You’ll nonetheless want a site knowledgeable and an
architect to vet, contextualize, and information the migration course of. AI alone
is not going emigrate a complete challenge seamlessly; it requires
considerate decomposition, clear boundaries, and iterative validation.

Not all these necessities will essentially be integrated into the
goal system, for instance the power to print a prescription sheet based mostly
on the drugs prescribed is deferred for now.

On this case, I augmented the evaluation with pattern responses from the
FHIR endpoint and whereas discarding features of the system that aren’t
related to the modernization effort. This consists of efficiency
optimizations, take a look at circumstances that aren’t straight related to the migration,
and configuration choices such because the variety of rows to show and
whether or not to indicate lively or inactive drugs. I felt these could be
addressed as a part of the following iteration.

For example, take into account the unit take a look at eventualities outlined for rendering
remedy information:

        ✅ Completely happy Path

        It ought to appropriately render the drugName column.
        It ought to appropriately render the standing column with the suitable Tag colour.
        It ought to appropriately render the precedence column with the right precedence Tag.
        It ought to appropriately render the supplier column.
        It ought to appropriately render the startDate column.
        It ought to appropriately render the length column.
        It ought to appropriately render the frequency column.
        It ought to appropriately render the route column.
        It ought to appropriately render the doseQuantity column.
        It ought to appropriately render the instruction column.

        ❌ Unhappy Path

        It ought to present a “-” if startDate is lacking.
        It ought to present a “-” if frequency is lacking.
        It ought to present a “-” if route is lacking.
        It ought to present a “-” if doseQuantity is lacking.
        It ought to present a “-” if instruction is lacking.
        It ought to deal with circumstances the place the row information is undefined or null.
      

Changing lacking values with “-” within the unhappy path eventualities has been eliminated,
because it doesn’t align with the necessities of the goal system. Such selections
must be guided by enter from the subject material consultants (SMEs) and
stakeholders, making certain that solely performance related to the present enterprise
context is retained.

The literature gathered on the show management now must be coupled with
challenge conventions, practices, and pointers with out which the LLM is open to
interpret the above request, on the information that it was educated with. This consists of
entry to features that may be reused, pattern information fashions and companies and
reusable atomic elements that the LLMs can now depend on. If such practices,
fashion guides and pointers should not clearly outlined, each iteration of the
migration dangers producing non-conforming code. Over time, this could contribute to
a fragmented codebase and an accumulation of technical debt.

The core goal is to outline clear, project-specific coding requirements and
fashion guides to make sure consistency within the generated code. These requirements act as
a foundational reference for the LLM, enabling it to supply output that aligns
with established conventions. For instance, the Google TypeScript Fashion Information can
be summarized and documented as a TypeScript fashion information saved within the goal
codebase. This file is then learn by Cline firstly of every session to make sure
that each one generated TypeScript code adheres to a constant and acknowledged
commonplace.

Rebuild

Rebuilding the function for a goal system with LLM-generated code is
the ultimate section of the workflow. Now with all of the required information gathered,
we are able to get began with a easy immediate

You’re tasked with constructing a Remedy show management within the new react ts fhir frontend. You could find the main points of the legacy Remedy show management implementation in docs/treatments-legacy-implementation.md. Create the brand new show management by following the docs/display-control-guide.md

At this stage, the LLM generates the preliminary code and take a look at eventualities,
leveraging the knowledge offered. As soon as this output is produced, it’s
important for area consultants and builders to conduct a radical code evaluation
and apply any needed refactoring to make sure alignment with challenge requirements,
performance necessities, and long-term maintainability.

Refactoring the LLM-generated code is essential to making sure the code stays
clear and maintainable. With out correct refactoring, the end result might be a
disorganized assortment of code fragments quite than a cohesive, environment friendly
system. Given the probabilistic nature of LLMs and the potential discrepancies
between the generated code and the unique targets, it’s important to
contain area consultants and SMEs at this stage. Their position is to completely
evaluation the code, validate that the output aligns with the preliminary expectations,
and assess whether or not the migration has been efficiently executed. This knowledgeable
involvement is essential to make sure the standard, accuracy, and total success of
the migration course of.

This section must be approached as a complete code evaluation—just like
reviewing the work of a senior developer who possesses sturdy language and
framework experience however lacks familiarity with the precise challenge context.
Whereas technical proficiency is important, constructing sturdy programs requires a
deeper understanding of domain-specific nuances, architectural selections, and
long-term maintainability. On this context, the human-in-the-loop performs a
pivotal position, bringing the contextual consciousness and system-level understanding
that automated instruments or LLMs might lack. It’s a essential course of to make sure that
the generated code integrates seamlessly with the broader system structure
and aligns with project-specific necessities.

In our case, the intent and context of the rebuild have been clearly outlined,
which minimized the necessity for post-review refactoring. The necessities gathered
in the course of the analysis section—mixed with clearly articulated challenge conventions,
expertise stack, coding requirements, and elegance guides—ensured that the LLM had
minimal ambiguity when producing code. Because of this, there was little left for
the LLM to deduce independently.

That mentioned, any unresolved questions concerning the implementation plan can
result in deviations from the anticipated output. Whereas it’s not possible to
anticipate and reply each such query prematurely, it is very important
acknowledge the inevitability of “unknown unknowns.” That is exactly the place a
thorough evaluation turns into important.

On this explicit occasion, my familiarity with the show management we have been
rebuilding allowed me to proactively decrease such unknowns. Nevertheless, this degree
of context might not at all times be accessible. Subsequently, I strongly suggest
conducting an in depth code evaluation to assist uncover these hidden gaps. If
recurring points are recognized, the immediate can then be refined to deal with them
preemptively in future iterations.

The attract of LLMs is simple; they provide a seemingly easy answer
to advanced issues, and builders can typically create such an answer rapidly and
while not having years of deep coding expertise. This could not create a bias
within the consultants, succumbing to the attract of LLMs and finally take their palms
off the wheel.

Consequence

Determine 4: A excessive degree overview of the method; taking a function from the legacy codebase and utilizing LLM-assisted evaluation to rebuild it inside the goal system

In my case the code era course of took about 10 minutes to
full. The evaluation and implementation, together with each unit and
integration exams with roughly 95% protection, have been accomplished utilizing
Claude 3.5 Sonnet (20241022). The full price for this effort was about
$2.

Determine 5: Legacy Therapies Show Management constructed utilizing Angular and built-in with OpenMRS REST endpoints

Determine 6: Modernized Therapies Show Management rebuilt
utilizing React and TypeScript, leveraging FHIR endpoints

With out AI help, each the technical evaluation and implementation
would have seemingly taken a developer a minimal of two to a few days. In my
case, creating a reusable, general-purpose immediate—grounded within the shared
architectural rules behind the roughly 30 show controls in
Bahmni—took about 5 targeted iterations over 4 hours, at a barely
increased inference price of round $10 throughout these cycles. This effort was
important to make sure the generated immediate was modular and broadly
relevant, given that every show management in Bahmni is basically a
configurable, embeddable widget designed to boost system flexibility
throughout completely different medical dashboards.

Even with AI-assisted era, one of many key prices in improvement
stays the time and cognitive load required to investigate, evaluation, and
validate the output. Because of my prior expertise with Bahmni, I used to be in a position
to evaluation the generated evaluation in beneath quarter-hour, supplementing it
with fast parallel analysis to validate the claims and information mappings. I
was pleasantly stunned by the standard of the evaluation: the information mannequin
mapping was exact, the logic for transformation was sound, and the take a look at
case solutions lined a complete vary of eventualities, each typical
and edge circumstances.

Code evaluation, nevertheless, emerged as probably the most important problem.
Reviewing the generated code line by line throughout all modifications took me
roughly 20 minutes. In contrast to pairing with a human developer—the place
iterative discussions happen at a manageable tempo—working with an AI system
able to producing complete modules inside seconds creates a bottleneck
on the human aspect, particularly when making an attempt line-by-line scrutiny. This
isn’t a limitation of the AI itself, however quite a mirrored image of human
evaluation capability. Whereas AI-assisted code reviewers are sometimes proposed as a
answer, they’ll typically establish syntactic points, adherence to greatest
practices, and potential anti-patterns—however they battle to evaluate intent,
which is essential in legacy migration initiatives. This intent, grounded in
area context and enterprise logic, should nonetheless be confirmed by the human in
the loop.

For a legacy modernization challenge involving a migration from AngularJS
to React, I might price this expertise an absolute 10/10. This functionality
opens up the chance for any people with first rate technical
experience and robust area data emigrate any legacy codebase to a
trendy stack with minimal effort and in considerably much less time.

I consider that with a bottom-up method, breaking the issue down
into atomic elements, and clearly defining greatest practices and
pointers, AI-generated code may drastically speed up supply
timelines—even for advanced brownfield initiatives as we noticed for Bahmni.

The preliminary evaluation and the next evaluation by consultants leads to a
crisp sufficient doc that lets us use the restricted house within the context
window in an environment friendly approach so we are able to match extra info into one single
immediate. Successfully, this enables the LLM to investigate code in a approach that’s
not restricted by how the code is organized within the first place by builders.
This additionally leads to decreasing the general price of utilizing LLMs, as a brute
power method would imply that you simply spend 10 occasions as a lot even for a a lot
easier challenge.

Whereas modernizing the legacy codebase is the principle product of this
proposed method, it’s not the one beneficial one. The documentation
generated concerning the system is effective when offered not simply to the tip
customers / implementers in complementing or filling gaps in current programs
documentation and likewise would stand in as a data base concerning the system
for ahead engineering groups pairing with LLMs to boost or enrich
system capabilities.

Why the Assessment Section Issues

A key enabler of this profitable migration was a well-structured plan
and detailed scope evaluation section previous to implementation. This early
funding paid dividends in the course of the code era section. And not using a
clear understanding of the information movement, configuration construction, and
show logic, the AI would have struggled to supply coherent and
maintainable outputs. You probably have labored with AI earlier than, you might have
observed that it’s constantly desirous to generate output. In an earlier
try, I proceeded with out enough warning and skipped the evaluation
step—solely to find that the generated code included a useMemo hook
for an operation that was computationally trivial. One of many success
standards within the generated evaluation was that the code must be
performant, and this seemed to be the AI’s approach of fulfilling that
requirement.

Curiously, the AI even added unit exams to validate the
efficiency of that particular operation. Nevertheless, none of this was
explicitly required. It arose solely attributable to a poorly outlined intent. AI
integrated these modifications with out hesitation, regardless of not totally
understanding the underlying necessities or searching for clarification.
Reviewing each the generated evaluation and the corresponding code ensures
that unintended additions are recognized early and that deviations from
the unique expectations are minimized.

Assessment additionally performs a key position in avoiding pointless back-and-forth
with the AI in the course of the rebuild section. For example, whereas refining the
immediate for the “Show Management Implementation
Information”,
I initially didn’t have the part specifying the unit exams to be
included. Because of this, the AI generated a take a look at that was largely
meaningless—providing a false sense of take a look at protection with no actual
connection to the code beneath take a look at.

Determine 7: AI generated unit take a look at that verifies actuality
remains to be actual

In an try to repair this take a look at, I started prompting
extensively—offering examples and detailed directions on how the unit
take a look at must be structured. Nevertheless, the extra I prompted, the additional the
course of deviated from the unique goal of rebuilding the show
management. The main target shifted fully to resolving unit take a look at points, with
the AI even starting to evaluation unrelated exams within the codebase and
suggesting fixes for issues it recognized there.

Finally, realizing the rising divergence from the meant
job, I restarted the method with clearly outlined directions from the
outset, which proved to be far simpler.

This leads us to a vital perception: Do not Interrupt AI.

LLMs, at their core, are predictive sequence turbines that construct
narratives token by token. Once you interrupt a mannequin mid-stream to
course-correct, you break the logical movement it was establishing.
Stanford’s “Misplaced within the
Center”
examine revealed that fashions can undergo as much as a 20%
drop in accuracy when essential info is buried in the midst of
lengthy contexts, versus when it’s clearly framed upfront. This underscores
why beginning with a well-defined immediate and letting the AI full its
job unimpeded typically yields higher outcomes than fixed backtracking or
mid-flight corrections.

This concept can be strengthened in “Why Human Intent Issues Extra as AI
Capabilities Develop” by Nick
Baumann,
which argues that as mannequin capabilities scale, clear human intent—not
simply brute mannequin energy—turns into the important thing to unlocking helpful output.
Quite than micromanaging each response, practitioners profit most by
designing clear, unambiguous setups and letting the AI full the arc
with out interruption.

Conclusion

You will need to make clear that this method will not be meant to be a
silver bullet able to executing a large-scale migration with out
oversight. Quite, its energy lies in its capacity to considerably
cut back improvement time—doubtlessly by a number of weeks—whereas sustaining
high quality and management.

The purpose is not to switch human experience however to amplify it—to
speed up supply timelines whereas making certain that high quality and
maintainability are preserved, if not improved, in the course of the transition.

It is usually vital to notice that the expertise and outcomes mentioned
up to now are restricted to read-only controls. Extra advanced or interactive
elements might current further challenges that require additional
analysis and refinement of the prompts used.

One of many key insights from exploring GenAI for legacy migration is
that whereas massive language fashions (LLMs) excel at general-purpose duties and
predefined workflows, their true potential in large-scale enterprise
transformation is simply realized when guided by human experience. That is
nicely illustrated by Moravec’s Paradox, which observes that duties perceived
as intellectually advanced—comparable to logical reasoning—are comparatively simpler
for AI, whereas duties requiring human instinct and contextual
understanding stay difficult. Within the context of legacy modernization,
this paradox reinforces the significance of material consultants (SMEs)
and area specialists, whose deep expertise, contextual understanding,
and instinct are indispensable. Their experience allows extra correct
interpretation of necessities, validation of AI-generated outputs, and
knowledgeable decision-making—in the end making certain that the transformation is
aligned with the group’s targets and constraints.

Whereas project-specific complexities might render this method bold,
I consider that by adopting this structured workflow, AI-generated code can
considerably speed up supply timelines—even within the context of advanced
brownfield initiatives. The intent is to not substitute human experience, however to
increase it—streamlining improvement whereas safeguarding, and doubtlessly
enhancing, code high quality and maintainability. Though the standard and
architectural soundness of the legacy system stay essential elements, this
methodology gives a powerful start line. It reduces guide overhead,
creates ahead momentum, and lays the groundwork for cleaner and extra
maintainable implementations via expert-led, guided refactoring.

I firmly consider following this workflow opens up the chance for
any people with first rate technical experience and robust area
data emigrate any legacy codebase to a contemporary stack with minimal
effort and in considerably much less time.