The directory

Choose chunk sizes, overlaps, and structure-aware splits so retrieved context actually helps the model answer.

Combine BM25-style lexical search with dense embeddings and fuse results so both rare terms and paraphrases are found.

Score groundedness, relevance, and completeness with judge prompts plus deterministic checks wired into CI.

Evaluate multilingual RAG before changing retrievers, models, or prompts: measure language-specific answer quality, cross-lingual retrieval, and cultural fit wi

Build a multilingual RAG evaluation report in Node.js when you need reproducible per-language retrieval metrics and release gates; start with the dependency-fre

Shape tool names, parameters, descriptions, and error messages so the agent calls the right tool with valid arguments.

Bound agent autonomy with step budgets, spend caps, human gates, and checkpointing so failures stay cheap and debuggable.

Split work across specialized agents with typed handoff contracts so context survives delegation without ballooning.

Organize identity, rules, tools, and output contracts into sections that evolve without breaking behavior.

Choose and order examples covering edge cases so the model generalizes instead of copying surface patterns.

Choose and order examples covering edge cases so the model generalizes instead of copying surface patterns.

Get machine-parseable JSON reliably using schema-first prompting, constrained decoding, and repair loops.

Clean, deduplicate, and balance instruction-response pairs so fine-tuning learns behavior rather than noise.

Pick the cheapest adaptation layer that solves the problem using a decision ladder.

Allocate milliseconds across retrieval, prompting, generation, and streaming so p95 meets product targets.

Route by task complexity, cache aggressively, and enforce budgets so unit economics hold as usage grows.

Treat all retrieved content as untrusted input: isolate instructions from data, gate actions, and fuzz continuously.

Streaming ASR, incremental TTS, and barge-in handling keeping conversation feel under 800ms response.

Task-grounded scoring for image/chart/document understanding with calibrated human baselines.

Working, episodic, and semantic memory tiers with promotion/forgetting policies that stay coherent.

Priority-ranked context assembly with measured attention degradation at depth.

PTQ vs QAT tradeoffs, per-layer sensitivity analysis, and eval gates protecting downstream task accuracy.

Paged attention, prefix reuse, and eviction policies turning long-context from cost center into feature.

Teacher selection, data generation governance, and student eval harnesses preserving target behaviors.