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DE🧠 Here’s how i would debug 👇
1️⃣ Context formatting / prompt template
Check how the retrieved chunks are inserted into the prompt. Sometimes the context is poorly formatted, truncated, or the instruction doesn’t clearly tell the model to rely on the provided documents.
2️⃣ Chunk relevance inside retrieved docs
Even if the right document is retrieved, the exact chunk sent to the model might not contain the answer. Verify chunk size, overlap, and whether the important sentence is actually present in the context.
3️⃣ Context window limits
Ensure the retrieved documents are not getting cut off due to token limits. If too many chunks are sent, the model may ignore the relevant one.
4️⃣ Ranking / reranking step
Check if the most relevant chunk is placed at the top of the context. LLMs often give more attention to earlier content.
5️⃣ Instruction clarity
Verify that the prompt explicitly says something like:
“Answer only using the provided context. If the answer is not present, say you don’t know.”
6️⃣ Model temperature / generation settings
High temperature can cause hallucinations even when the answer is present. Try temperature 0–0.2 for factual RAG tasks.
7️⃣ Grounding verification
Test by asking the model to cite the source chunk or quote the sentence it used. If it cannot, it’s likely ignoring the retrieved context
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