MarketAlly.AIPlugin.Extensions/MarketAlly.AIPlugin.Learning/AI_LOG/LLM_NEEDS.md

Where LLMs Actually Need This

1. Context Window Limitations

• Even Claude-3.5 with 200k tokens can't hold entire enterprise codebases
• Need intelligent chunking and retrieval to feed relevant code sections
• Your indexing infrastructure solves this perfectly

2. Real-time Code Understanding

• LLMs can't maintain "memory" of codebase structure across conversations
• Need persistent relationship mapping (call graphs, dependencies, inheritance)
• Your Roslyn analysis captures this beautifully

3. Performance & Cost

• Sending entire codebases to LLMs repeatedly is expensive/slow
• Need smart pre-filtering to identify relevant code sections
• Your indexing enables surgical context selection

The Pivot That Makes Sense

Transform from "embedding search tool" to "LLM context preparation service":

// Instead of this var similar = await SearchSimilarAsync("authentication logic");

// Do this var context = await PrepareContextAsync("implement OAuth", maxTokens: 8000); var response = await llm.AnalyzeWithContext(context, userQuery);

Modern LLM Integration Patterns

What's actually valuable:

• Smart chunking: Break code into semantically coherent pieces
• Dependency tracking: "If user asks about X, also include Y and Z"
• Change impact analysis: "These 47 files might be affected"
• Code relationship mapping: "Here are all the callers of this method"

Your infrastructure does all of this. Just ditch the embeddings and become the intelligent code context engine that feeds LLMs exactly what they need.

That's genuinely valuable and has no good alternatives.