Sigma Assistant
Upgrade Plan
Turn a strong real-world AI product story into crisp, defensible technical reasoning—one decision, mechanism, measurement, and consequence at a time.
Four beats in every answer
Decision
What consequential technical choice did the team face?
Alternatives
What credible paths did you consider?
Evidence
Which measurements and constraints shaped the choice?
Consequence
What improved, what worsened, and what would you revisit?
The 90-second opening
The most consequential trade-off was quality versus latency in grounding and validation. More retrieval and reasoning improved semantic correctness, but increased response time and cost. We set an interaction budget, tested alternatives against representative evals, and kept the steps whose quality lift justified their latency. I’ll walk through the architecture, then the evidence behind that decision.”
Use the structure—not invented precision. Replace every model, metric, threshold, and result with facts you personally remember and can defend.
End-to-end architecture
For every box, prepare five answers: why it exists, what fails, how access is enforced, how it is measured, and what happens at 100× scale.
Executable SQL ≠ correct answer
Make hallucination measurable
Failure taxonomy
Invented schema • wrong join • unsupported metric • wrong filter or aggregation • plausible but semantically wrong SQL • unauthorized access attempt.
Evaluation design
Representative categories • defined difficulty tiers • result-set comparison • rubric-based semantic grading • severity-weighted errors.
Operating loop
Regression gates • per-category pass rates • production feedback converted into eval cases • monitoring by impact, not anecdotes.
Be ready to define “accuracy”: the evaluation unit, grader, dataset composition, category distribution, and which failures are unacceptable regardless of the aggregate score.
Layer safety—don’t outsource it to a disclaimer
Prevent
Authorization-scoped context, privacy filtering, read-only operations, prompt-injection defenses.
Detect
Parsing, policy rules, execution checks, semantic graders, anomaly detection, confidence signals.
Contain
Sandboxing, timeouts, resource ceilings, per-tenant isolation, staged exposure, rate limits.
Recover
Clarify ambiguity, bounded repair retries, or abstain rather than manufacture certainty.
Explain
Surface tables, joins, filters, assumptions, metric definitions, and result drill-down.
Remember
A summary from the same model improves usability; it is not independent verification.
Explain RAG as a product decision
Know the mechanism
Corpus ownership, embeddings, nearest-neighbor search, metadata filters, threshold, fallback.
Know the risk
An irrelevant example can anchor generation toward the wrong tables, joins, or definitions.
Know the evidence
Quality lift and latency cost by query category—not “RAG helped” as an unsupported claim.
Make the trade-off visual and explicit
More grounding
Richer retrieval, more context, stronger reasoning and validation.
Upside: semantic quality
Cost: latency, tokens, complexity
Fast interaction
Lean context, limited retrieval, streaming, bounded retries.
Upside: responsiveness
Cost: weaker grounding or premature output
Prepare for 100× scale
Name the likely bottleneck, the metric that proves it, and the first three mitigations. Consider provider limits, queue depth, warehouse saturation, long-running queries, connection pools, retry amplification, noisy neighbors, and token cost from large schemas.
Build where the product differentiates
Buy or reuse
Foundation model
Vector-search infrastructure
Workflow orchestration
Data warehouse
Internal privacy/model proxy
Build
Authorization-aware schema context
Verified retrieval corpus
Prompt and context assembly
Validation and repair workflow
Product-specific eval suite
Business definitions
Monitoring and feedback loop
The strategic point: differentiation lives in trusted context, evaluations, workflow, and user experience—not merely access to a foundation model.
Complete the fact sheet
Write “unknown” when you do not remember. A strong line is: “I don’t remember the exact value, but the decision rule was…”
Five focused drills
Architecture
Deliver the request path without interruption. End by naming the trade-off.
Correctness
Explain why executable SQL can be wrong using the four evaluation layers.
Trade-off
Use one concrete decision and its measured quality, latency, cost, or privacy consequence.
Failure recovery
Cover bad retrieval, ambiguous intent, and unauthorized access: detect, contain, recover.
100× scale
Name the likely bottleneck, the proving metric, and the first three mitigations.