Finding Code Logic with AI Assistance | Generated by AI
Question: Where is the “search query” logic located in my ww llm search code, and can tools like Claude Code help me read and understand the code?
Answer:
Yes — using an AI coding assistant like Claude Code can absolutely help you explore and understand a codebase faster. But it works best when you guide it with the right strategy instead of relying on blind pattern searches.
Let’s break this into two parts: how to locate the “search query” logic, and how to effectively read code with AI assistance.
1. Where is the “search query” logic usually located?
When you see something like:
Searching for 2 patterns…
⎿ "ww/llm/*.py"
That’s just a file filter, not the actual search logic.
To find the real “search query” part, think in terms of intent, not file name. In an LLM or search-related system, the logic is usually in one of these places:
A. Query construction
Look for code that:
- builds prompts
- formats user input
- adds context (RAG, embeddings, etc.)
Common keywords to search:
query
prompt
search
retrieve
embedding
rag
vector
B. Retrieval / search backend
If your project uses search (especially LLM + knowledge base), check for:
- vector DB calls (FAISS, Chroma, Pinecone)
- keyword search (Elasticsearch, SQL LIKE)
- API calls
Search for:
similarity_search
search_documents
retrieve
client.query
C. Entry points (very important)
Often the logic is not in llm/ but in:
- CLI entry files
- API handlers
- service layer
Search for:
main(
handle_request
chat(
ask(
2. How to use Claude Code effectively (this is the real skill)
You’re right: AI can help — but how you ask matters more than the tool itself.
Instead of:
“tell me where search query is”
Use progressive narrowing:
Step 1: Map the structure
Ask:
Give me an overview of the ww project structure and what each folder does
Step 2: Trace execution flow
When I run this CLI, what is the call chain?
Step 3: Locate query generation
Where is user input turned into a search query or LLM prompt?
Step 4: Dive deeper
Explain how this function constructs the query
3. Key insight (this is important)
What you’re doing is shifting from:
❌ “search files by name”
to:
✅ “trace data flow”
That’s a huge upgrade in engineering thinking.
4. Practical workflow (very effective)
Combine tools:
- Claude Code → reasoning + explanation
grep/rg→ fast pattern search- Your brain → understand architecture
Example:
rg "query" ww/
rg "search" ww/
rg "prompt" ww/
Then ask Claude:
These 3 files look related — which one actually builds the search query?
5. Final perspective
AI tools like Claude Code are powerful, but they don’t replace thinking — they amplify structured thinking.
If you:
- follow execution flow
- understand layers (input → processing → output)
- ask precise questions
Then AI becomes like a senior engineer sitting next to you.
If you want, paste your ww project structure or a few files — I can help you pinpoint the exact “search query” location precisely.