Retrieval-Augmented Generation (RAG) Questions

To solve this, I’d use a greedy selection strategy based on relevance per token, while keeping traceability metadata attached to every chunk.

def build_context_package(chunks, token_budget):
    """
    chunks: list of dicts with keys:
      - text
      - relevance_score
      - token_estimate
      - source_metadata
    returns: selected chunks and total tokens
    """
    # Sort by highest relevance density first
    ranked = sorted(
        chunks,
        key=lambda c: (c["relevance_score"] / max(c["token_estimate"], 1)),
        reverse=True,
    )

    selected = []
    used_tokens = 0

    for chunk in ranked:
        tokens = chunk["token_estimate"]
        if used_tokens + tokens <= token_budget:
            selected.append({
                "text": chunk["text"],
                "source_metadata": chunk["source_metadata"],
                "relevance_score": chunk["relevance_score"],
                "token_estimate": tokens,
            })
            used_tokens += tokens

    return {"chunks": selected, "total_tokens": used_tokens}

Key points

• I optimize for signal per token, not just raw relevance.
• Metadata stays with each chunk for citations and auditability.
• This is easy to explain and debug.

Complexity

• Time: O(n log n) from sorting
• Space: O(n) for the selected package

A good enhancement would be reserving tokens for instructions and a fallback summary chunk.

I’d design this as a guarded copilot, not a free-form text-to-SQL toy.

Architecture
Business user → auth layer → semantic layer/metadata store → LLM planner → SQL validator → warehouse → charting/narrative service.

Retrieval and permissions

• Retrieve only approved table definitions, metrics, and column descriptions
• Enforce row-level and column-level security before query generation
• Pass the user’s permissions into the prompt so the model cannot ask for unauthorized data

Latency and cost

• Cache common metadata and use smaller models for routing
• Generate SQL once, then validate before execution
• Stream partial responses for narrative text, but only render charts after query success

Validation

• Parse SQL against a whitelist of safe patterns
• Reject dangerous operations like DROP, UPDATE, or unrestricted joins
• Compare query results against schema expectations and sanity checks

Human override

• Let users edit SQL before execution
• Show citations, query preview, and confidence warnings
• Provide a fallback to manual report templates when the model is uncertain

This approach keeps the copilot useful for business users while respecting governance and cost constraints.

I’d treat prompt injection as a security problem across three attack surfaces: user input, retrieved content, and tool output.

Attack surfaces

• Direct injection: a user tries to override system instructions
• Indirect injection: a retrieved document contains hidden malicious instructions
• Exfiltration: the model is tricked into revealing secrets, system prompts, or private context

Defenses

• Separate instructions from data in the prompt template
• Sanitize and classify retrieved documents before they reach the model
• Use a policy layer that blocks tool calls outside allowed scopes
• Never place secrets, API keys, or hidden prompts in model context
• Limit retrieval to trusted sources and use content provenance metadata
• Add output filters to detect sensitive data leakage or unauthorized summaries

Detection

• Log suspicious prompt patterns, unusual tool usage, and high-risk refusals
• Red-team with adversarial prompts and poisoned documents
• Monitor for sudden drops in groundedness or spikes in blocked actions

The main principle is simple: the model should never have the power to access or reveal more than the user is entitled to see.

For changing policy documents, I would usually start with RAG, and only fine-tune if I had a stable, repeated task that benefits from a consistent writing style or classification behavior.

Why RAG first

• Policies change often, so retrieval keeps answers current without retraining.
• It preserves source citations, which matters for compliance and trust.
• It is easier to update and debug because I can inspect the retrieved passages.

When fine-tuning makes sense

• The question type is narrow and repetitive, like routing or policy classification.
• The organization has enough high-quality labeled examples.
• The model must follow a very specific tone or response format.

Decision factors

• Update frequency of documents
• Need for citations and traceability
• Volume of labeled training data
• Latency and cost constraints
• Risk tolerance for outdated answers

For most policy Q&A systems, I’d ship RAG, measure answer accuracy and citation quality, then consider fine-tuning only for prompt-following or structured extraction improvements.

I’d use a hybrid retrieval strategy because the assistant needs to answer from both structured and unstructured sources.

Warehouse tables

• Use SQL retrieval or semantic layer metrics for exact facts like revenue, churn, and cohort counts.
• Prefer deterministic queries over embeddings when the question maps cleanly to schema.

PDFs and product docs

• Chunk by section headings, not fixed character counts, so definitions stay intact.
• Use overlapping chunks for long procedures, but keep overlap small to avoid duplication.
• Store source, version, and page number for citations.

Retrieval strategy

• Route the query first: metric question to SQL, policy/process question to document search, mixed question to both.
• Merge results with a reranker so the most relevant evidence appears first.
• Use freshness-aware indexing and re-embed documents when a version changes.

Reliability

• Keep a document registry with timestamps and owners.
• Show citations in the answer.
• Rebuild the index incrementally so updates are fast and auditable.

That approach gives accurate analytics answers while keeping the knowledge base maintainable.