I'm building a retrieval-augmented app and want to see which chunks were used

To see which chunks were used in a retrieval-augmented generation (RAG) app, you typically need to surface the retrieval step’s results alongside the final answer.

What to track

For each user query, log:

• Retrieved chunk text
• Chunk metadata: source doc, page/section, chunk id, score
• Prompt sent to the model
• Final answer
• Optional: re-ranking results and citations

Common ways to expose it

1. Return retrieved chunks in the API response

   • Useful for debugging and transparency.
   • Example response shape:

   {
     "answer": "…",
     "retrieved_chunks": [
       {
         "chunk_id": "doc1_03",
         "source": "doc1.pdf",
         "score": 0.82,
         "text": "..."
       }
     ]
   }
   

2. Add citations in the answer

   • e.g. [1], [2] linked to chunk metadata.

3. Store retrieval traces

   • Save query, top-k results, and prompt context in a database or log system.

4. Use a tracing/observability tool

   • LangSmith, Weights & Biases, OpenTelemetry, etc.

Minimal implementation pattern

If you’re using a vector DB, your flow is usually:

results = vector_store.similarity_search(query, k=5)

context = "\n\n".join([r.page_content for r in results])

answer = llm.generate(prompt=query + context)

return {
    "answer": answer,
    "retrieved_chunks": [
        {
            "text": r.page_content,
            "metadata": r.metadata
        }
        for r in results
    ]
}

If you want to inspect them during development

• Print the top-k results before generation
• Log similarity scores
• Save them to a file or dashboard
• Show a “sources” panel in your UI

If you tell me what stack you’re using — e.g. LangChain, LlamaIndex, OpenAI Assistants, Pinecone, FAISS, Chroma, Weaviate — I can show exactly how to extract and display the chunks.