How to Fine-Tune the LLM for Specific Project Domains in Understand-Anything

Fine-tune the LLM for specific project domains in Understand-Anything by injecting domain-specific context into the prompt builders (buildFileAnalysisPrompt, buildProjectSummaryPrompt) or by routing requests to a custom fine-tuned model endpoint via the UNDERSTAND_LLM_ENDPOINT environment variable.

Understand-Anything treats the LLM as a stateless prompt-engine that produces JSON-structured analyses of source files and entire projects. To fine-tune the LLM for specific project domains—such as fintech, gaming, or healthcare—you leverage either prompt engineering or model substitution strategies without modifying the underlying graph-construction logic.

Two Strategies for Domain-Specific Tuning

The repository exposes two complementary approaches for tailoring LLM output to specialized domains. Both methods preserve the existing JSON parsing and validation pipeline while biasing the model toward domain-relevant terminology and architectural patterns.

Prompt Engineering with Domain Context

The LLM Analyzer (packages/core/src/analyzer/llm-analyzer.ts) constructs prompts through buildFileAnalysisPrompt and buildProjectSummaryPrompt. These functions accept a projectContext parameter that is prepended to every LLM request. By supplying rich descriptions of your domain—including key vocabularies, typical architectural layers, and file-naming conventions—you guide the model to generate analyses that reflect domain-specific knowledge.

Model Substitution and Fine-Tuned Endpoints

Understand-Anything does not embed a specific LLM; the actual inference is performed by the surrounding Claude-Code/Claude-AI plugin. You can configure the plugin to call a custom endpoint (e.g., an OpenAI fine-tuned model or a private deployment) by setting the UNDERSTAND_LLM_ENDPOINT environment variable. This approach routes all LLM traffic to your domain-specialized model without touching the source code in packages/core/src/analyzer/llm-analyzer.ts.

Step-by-Step Implementation Guide

Follow these steps to implement domain-specific fine-tuning in your Understand-Anything workflow.

1. Gather domain context. Write a concise paragraph (2-3 sentences) describing the domain, key concepts, and special file-naming conventions. For example, fintech projects might emphasize PCI compliance, settlement flows, and AML checks.

2. Pass the context to the analyzer. Inject the domain description via the projectContext argument when calling buildFileAnalysisPrompt or buildProjectSummaryPrompt.

3. Configure a fine-tuned model endpoint. If you have a model fine-tuned on domain data, expose it via an HTTP endpoint and set the environment variable that the plugin reads:

   export UNDERSTAND_LLM_ENDPOINT="https://api.openai.com/v1/fine-tuned-models/finance-2024"

4. Validate the output. The JSON parsers parseFileAnalysisResponse and parseProjectSummaryResponse automatically reject malformed responses. Run the unit test suite to ensure compatibility:

   pnpm --filter @understand-anything/core test

5. Iterate. Adjust the domain context or refine the fine-tuned model until the tags, complexity ratings, and layer assignments match your expectations.

Code Examples

Extending the Prompt Builder with Domain Context

Create a domain-aware wrapper around the core prompt builders to inject contextual hints based on the project type:

// src/domain-prompt.ts
import { buildFileAnalysisPrompt } from "./analyzer/llm-analyzer";

/**
 * Wraps the basic prompt builder with configurable domain context.
 */
export function buildDomainFilePrompt(
  filePath: string,
  content: string,
  domain: "fintech" | "gaming" | "health"
): string {
  const contexts = {
    fintech: `
      The project is a financial‑technology system. Important concepts:
      • Payment processing, PCI compliance, AML checks.
      • Layers: API Gateway → Transaction Service → Ledger DB.
      • Files often include “settlement”, “account”, “risk”.`,
    gaming: `
      This project powers an online multiplayer game. Key ideas:
      • Real‑time matchmaking, player inventory, anti‑cheat.
      • Layers: Game Server → Matchmaking → Persistence.
      • Expect terms like “lobby”, “XP”, “spawn”.`,
    health: `
      The codebase is for a health‑care platform. Core concerns:
      • Patient records, HIPAA compliance, medical imaging.
      • Layers: API → Service → Secure DB.
      • Look for “PHI”, “FHIR”, “consent”.`,
  };

  const domainContext = contexts[domain];
  return buildFileAnalysisPrompt(filePath, content, domainContext);
}

Switching to a Fine-Tuned Model at Runtime

Implement a client that respects the UNDERSTAND_LLM_ENDPOINT environment variable to route requests to your custom model:

// src/llm-client.ts
import fetch from "node-fetch";

export async function invokeLLM(prompt: string): Promise<string> {
  const endpoint = process.env.UNDERSTAND_LLM_ENDPOINT ??
    "https://api.anthropic.com/v1/complete"; // fallback

  const resp = await fetch(endpoint, {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      Authorization: `Bearer ${process.env.LLM_API_KEY}`,
    },
    body: JSON.stringify({ prompt, max_tokens: 1024 }),
  });

  const data = await resp.json();
  return data.completion;
}

End-to-End Domain Analysis

Combine the domain prompt builder with the custom LLM client to analyze files within a specific vertical:

import { buildDomainFilePrompt } from "./domain-prompt";
import { invokeLLM } from "./llm-client";
import { parseFileAnalysisResponse } from "./analyzer/llm-analyzer";

async function analyzeFile(filePath: string, content: string) {
  const prompt = buildDomainFilePrompt(filePath, content, "fintech");
  const raw = await invokeLLM(prompt);
  const analysis = parseFileAnalysisResponse(raw);
  console.log("LLM analysis →", analysis);
}

Key Files and Architecture

Understanding these core files helps you locate the correct injection points for domain customization:

• packages/core/src/analyzer/llm-analyzer.ts – Builds prompts via buildFileAnalysisPrompt and buildProjectSummaryPrompt, extracts JSON via parseFileAnalysisResponse, and validates LLM responses.
• packages/core/src/analyzer/layer-detector.ts – Generates prompts asking the LLM to define logical layers and parses the resulting JSON output.
• understand-anything-plugin/src/context-builder.ts – Assembles the per-file projectContext string before invoking the analyzer.
• packages/core/src/index.ts – Re-exports the analyzer utilities and exposes them to the plugin runtime.
• packages/core/src/__tests__/llm-analyzer.test.ts – Contains expected JSON shapes; use this as a sanity-check when modifying prompts.

Summary

• Fine-tune the LLM for specific project domains by injecting domain-specific context into the projectContext parameter of buildFileAnalysisPrompt or buildProjectSummaryPrompt.
• Route requests to custom models by setting the UNDERSTAND_LLM_ENDPOINT environment variable to point to a fine-tuned model hosted on OpenAI, Anthropic, or another provider.
• Validate changes using the existing test suite (pnpm --filter @understand-anything/core test) to ensure JSON outputs remain compatible with parseFileAnalysisResponse.
• Preserve core logic by modifying only the prompt construction layer or the external endpoint configuration, leaving the graph-construction pipeline in packages/core/src/analyzer/ unchanged.

Frequently Asked Questions

Do I need to modify the core source code to add domain context?

No. You can inject domain context through the projectContext parameter available in buildFileAnalysisPrompt and buildProjectSummaryPrompt without altering the source files in packages/core/src/analyzer/. Alternatively, configure the UNDERSTAND_LLM_ENDPOINT environment variable to point to an external fine-tuned model, which requires zero code changes.

What format should the domain context take?

The domain context should be a concise string (2-3 sentences or bullet points) describing key terminology, architectural patterns, and file-naming conventions specific to your vertical. This text is prepended to the LLM prompt, so clear, structured descriptions yield better results than unstructured paragraphs.

Can I use a locally hosted fine-tuned model?

Yes. Any model exposed via an HTTP endpoint that accepts JSON prompts and returns text completions will work. Set UNDERSTAND_LLM_ENDPOINT to your local URL (e.g., http://localhost:8000/v1/completions) and ensure the response format matches what parseFileAnalysisResponse expects.

How do I validate that my custom prompts still produce compatible output?

Run the unit test suite with pnpm --filter @understand-anything/core test. The parseFileAnalysisResponse and parseProjectSummaryResponse functions will throw errors if the LLM output deviates from the expected JSON schema, allowing you to iterate on your domain context or fine-tuned model until the output validates successfully.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}