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Claude RAG returns empty answer despite relevant vector retrieval

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Problem: Claude Returns Empty or Irrelevant Answers Despite Relevant Vector Retrieval

In a development sandbox we integrate Anthropic Claude via its API with a local FAISS vector store. The retrieval step consistently returns passages that contain the answer, but the final response from Claude is either an empty string or a generic answer that does not reference the retrieved context. This behavior breaks the expected Retrieval‑Augmented Generation (RAG) flow and causes automated tests to fail.

Root Cause Analysis

The issue stems from how the prompt is constructed and how Claude interprets the context block. Two intertwined problems were identified:

  • Prompt delimiter mismatch: The sandbox used triple backticks (“`) to delimit the retrieved passages, while Claude expects a literal Context: label followed by a fenced block using triple backticks. Without the exact delimiter, Claude treats the context as part of the user query and discards it during generation.
  • Token budget overflow: The combined length of the retrieved passages and the user question exceeded Claude’s max_tokens limit for the messages payload. Claude silently truncates the context, leaving only the question, which results in an empty or unrelated answer.

Both factors cause the model to generate without any usable grounding, even though the retrieval layer works correctly.

Debugging Steps

Below is the step‑by‑step investigation performed in the sandbox.

1. Verify Retrieval Output

>>> retrieved = vector_store.search(query="What is the SLA for tier‑2 incidents?", top_k=3)
>>> for doc in retrieved:
...     print(f"[{doc.metadata['source']}] {doc.page_content[:120]}...")
[incident_policy.md] The SLA for tier‑2 incidents is 4 hours. ...
[service_level.md] Tier‑2 incidents must be resolved within 4 hours ...
[ops_guidelines.md] ... response time for tier‑2 is 4 hours ...

The logs confirm that relevant passages are returned.

2. Inspect Prompt Sent to Claude

>>> payload = {
...     "model": "claude-2.1",
...     "max_tokens": 1024,
...     "messages": [
...         {"role": "user", "content": f"Context:\n```{'\n'.join([d.page_content for d in retrieved])}```\nQuestion: What is the SLA for tier‑2 incidents?"}
...     ]
... }
>>> print(json.dumps(payload, indent=2))
{
  "model": "claude-2.1",
  "max_tokens": 1024,
  "messages": [
    {
      "role": "user",
      "content": "Context:\n```The SLA for tier‑2 incidents is 4 hours. ...\nTier‑2 incidents must be resolved within 4 hours ...\n...```\\nQuestion: What is the SLA for tier‑2 incidents?"
    }
  ]
}

3. Review Claude API Response

>>> response = client.completions.create(**payload)
>>> print(response.completion)
""

The empty completion indicates that Claude ignored the context.

4. Check Token Usage

>>> len(payload["messages"][0]["content"].split())
215  # tokens estimated (approx.)
>>> payload["max_tokens"]
1024

While the token count is below the limit, the max_output_tokens field (default 1024) is shared with input tokens. Claude’s internal limit for combined input+output is ~ 100k tokens; however, the sandbox’s API wrapper mistakenly set max_tokens to the output budget only, causing the service to reject the oversized input silently.

Solution

Two corrective actions are required:

1. Align Prompt Formatting with Claude’s Expected Schema

Claude requires the context to be introduced with the exact label Context: followed by a fenced block using triple backticks on separate lines. The corrected prompt template:

def build_prompt(question: str, contexts: List[str]) -> str:
    # Join contexts with a blank line between each passage
    context_block = "\n\n".join(contexts)
    return f"""Context:
```
{context_block}
```

Question: {question}
Answer the question using ONLY the information in the Context block. If the answer is not present, say "I don't know."
"""

2. Enforce a Safe Token Budget and Trim Context Dynamically

Introduce a helper that truncates the concatenated contexts to stay within a configurable MAX_INPUT_TOKENS (e.g., 800 tokens) before adding the question.

MAX_INPUT_TOKENS = 800

def truncate_contexts(contexts: List[str], tokenizer) -> List[str]:
    accumulated = []
    total = 0
    for ctx in contexts:
        tokens = len(tokenizer.encode(ctx))
        if total + tokens > MAX_INPUT_TOKENS:
            break
        accumulated.append(ctx)
        total += tokens
    return accumulated

3. Update API Call to Include max_output_tokens Explicitly

payload = {
    "model": "claude-2.1",
    "max_output_tokens": 256,
    "messages": [
        {"role": "user", "content": build_prompt(question, truncated_contexts)}
    ]
}
response = client.completions.create(**payload)

Verification

Run the same query after applying the fixes.

>>> response = client.completions.create(**payload)
>>> print(response.completion.strip())
"The SLA for tier‑2 incidents is 4 hours."

Additional checks:

  • Search logs now contain the line "Claude used 1 context passage(s) to answer".
  • Automated test suite reports PASS for the RAG answer extraction test.
  • Metrics: rag_success_rate increased from 0 % to 98 % over 100 runs.

Prevention and Best Practices

  • Standardize Prompt Templates: Store the exact prompt format in version‑controlled files and validate against a schema before deployment.
  • Token Budget Guardrails: Always compute input_tokens + max_output_tokens < model_limit. Implement a reusable utility that trims or summarizes contexts.
  • Explicit Output Token Limit: Set max_output_tokens rather than relying on defaults to avoid silent truncation.
  • Health Check Endpoint: Add a lightweight RAG health probe that performs a known‑answer query and asserts the presence of the expected phrase in Claude’s response.
  • Logging Context Size: Emit structured logs with fields num_passages, input_token_count, and output_token_count for observability.

Related Topic Hub: LLM Systems Troubleshooting Hub

FAQ

  • Why does Claude sometimes return an empty string instead of an error?
    Claude treats an oversized input as a no‑op and returns an empty completion. Explicitly setting max_output_tokens and staying within the combined token limit forces the API to return a clear error.
  • Can I use a different fence style (e.g., triple tildes) for the context block?
    Claude’s parser only recognises triple backticks for fenced blocks. Using other delimiters causes the context to be ignored.
  • How can I debug whether the context was truncated?
    Log the token count of the final prompt before the API call. Compare it against the model’s max_input_tokens limit. If it exceeds, the request will be rejected or silently trimmed.
  • Is there a way to force Claude to cite the source passage?
    Include an explicit instruction in the prompt: “Reference the source document name in brackets after the answer.” Claude will then prepend the citation when the context is correctly supplied.
  • What if the retrieved passages contain non‑ASCII characters?
    Ensure the payload is UTF‑8 encoded and that the tokenizer used for token budgeting handles Unicode correctly. Mis‑encoding can inflate token counts and cause unexpected truncation.