Projects & Labs

A recommendation flow that uses conversation context, question relationships, semantic relevance, diversity, and prior exposure to rank useful follow-up questions.

• ›Build candidate pools from parent-child question relationships and the current conversation context.
• ›Balance semantic relevance with diversity so near-duplicate suggestions do not dominate the list.
• ›Penalize previously exposed candidates and generate replacements asynchronously when the pool becomes weak.

A document-processing strategy that divides large files into model-safe segments while preserving page order, structural context, and traceability to the source.

• ›Estimate a safe segment size, validate it against the processing boundary, and reduce it when the model rejects the input.
• ›Use a bounded search strategy to converge on a useful chunk size without repeated linear retries.
• ›Retain page and section references so downstream extraction remains attributable to the original document.

An evaluation harness for comparing a legacy AI workflow with a newer skill-based flow across quality, reliability, trace behavior, and failure handling.

• ›Create representative scenarios that cover normal behavior, ambiguity, tool failures, and fallback paths.
• ›Combine deterministic assertions with scored evaluations for outputs that allow more than one valid answer.
• ›Gate migration on regression evidence and inspect traces when aggregate scores hide different failure modes.

A retrieval-backed caching pattern that combines exact keys, semantic search, metadata filters, and freshness checks to reuse prior answers only when confidence is high.

• ›Normalize queries into stable cache keys while retaining the original query context.
• ›Use hybrid retrieval to find related prior answers when exact lookup misses.
• ›Enforce entity and freshness constraints; fall back to fresh retrieval when reuse is uncertain.

A precision-first routing pattern that sends ambiguous queries to a default provider and selects a specialist only when the available evidence supports that choice.

• ›Treat specialist routing as a high-confidence override rather than forced classification.
• ›Combine rules, lexical matching, semantic retrieval, and score margins to assess confidence.
• ›Evaluate per-provider precision and wrong-specialist routes, not just overall accuracy.

A feedback pattern that reviews routed queries, identifies weak decisions, and proposes better retrieval examples, with regression checks before updates are promoted.

• ›Review routing decisions in a background evaluation pass, outside the request path.
• ›Use LLM feedback to propose missing or weak examples rather than treating it as ground truth.
• ›Test proposed example changes against regression scenarios before promoting them.