Gets machine-parseable JSON out of an LLM reliably — prefer provider-enforced grammar (OpenAI `response_format:{type:"json_schema",strict:true}`, Anthropic tool-calling / `tool_choice:{type:"tool"}`, Gemini `responseSchema`) over a "respond in JSON" prompt, define the contract once as a Pydantic/Zod model and validate every response (never trust the string), use constrained decoding / grammars for open weights (Outlines, llguidance, llama.cpp GBNF, vLLM `guided_json`), keep schemas SHALLOW with required fields + enums + `additionalProperties:false`, and build a repair-and-retry loop that handles refusals, `length`/`max_tokens` truncation, and streaming partial JSON (`partial-json-parser`). Schema-guided generation eliminates parse-error retries; validate-and-repair is the backstop, not the primary mechanism.
Builds production data grids that stay fast and accessible at 10k–1M+ rows — decide server-side vs client-side sort/filter/paginate by the dataset-fits-in-memory test (client only under ~10k rows, otherwise push to the API and treat the table as a controlled view of server state), ROW-VIRTUALIZE with TanStack Virtual or react-window so only the visible window mounts (fixed estimateSize, overscan 5–10, measureElement for dynamic rows, contain:strict, and a real scroll container — never table-layout:auto over thousands of rows), build the headless logic with TanStack Table v8 (or AG Grid when you need pinning/grouping/enterprise out of the box), add column resize/reorder/pin, inline edit with optimistic update + rollback on error, row selection with a stable rowId, full keyboard nav with roving tabindex over an ARIA grid (role=grid/row/gridcell, aria-sort, aria-rowcount/aria-rowindex so virtualization stays announced), position:sticky headers, streaming CSV export that doesn't block the main thread, and explici
Builds semantic/vector search — pick an embedding model + dimensionality (and whether to truncate Matryoshka dims) and the matching distance metric (cosine/dot/L2, normalize to unit length so cosine == dot and IP is correct), an ANN index with the recall/latency/memory tradeoff understood (HNSW M/efConstruction/efSearch for low-latency RAM-resident; IVF-PQ nlist/nprobe/PQ for billion-scale compressed; flat/exact for <100k) in pgvector/Qdrant/Milvus/FAISS/Pinecone, chunking + overlap + per-chunk metadata for filtering, HYBRID retrieval fusing BM25 + dense by Reciprocal Rank Fusion (RRF, k≈60) not score addition, a cross-encoder/Cohere reranker over the top-50→k, correct pre-filter-vs-ANN interaction (filterable HNSW, not post-filter that starves k), and offline eval with recall@k / nDCG@10 / MRR against a labeled qrels set. Quantize (scalar/PQ) only after measuring recall loss; tune efSearch/nprobe to a recall target, not a guess.
Configures HTTP response security headers and a strict, nonce/hash-based Content-Security-Policy — script-src with a per-request nonce or sha256 hash plus 'strict-dynamic' (so you can drop host allowlists and 'unsafe-inline'), object-src 'none', base-uri 'none', frame-ancestors to control framing, a Report-Only rollout via report-to/report-uri before enforcing, plus HSTS with includeSubDomains+preload, X-Content-Type-Options: nosniff, Referrer-Policy, a deny-by-default Permissions-Policy, correct CORS (echo a single allowed origin, never wildcard '*' together with Access-Control-Allow-Credentials), and cookie flags Secure+HttpOnly+SameSite. Eliminates inline-script XSS sinks, clickjacking, MIME-sniffing, mixed content, and credentialed-CORS leaks by policy rather than per-bug patching.
Implements consumer-driven contract testing so services deploy independently without a full integration environment — the consumer's unit tests record concrete request/response expectations against a stub (Pact `pact-jvm`/`pact-js`/`pact-python`, or Spring Cloud Contract DSL), the resulting contract (pact file / Spring stub jar) is published to a broker (Pact Broker / PactFlow) tagged by consumer version + branch + environment, the provider replays every expectation against its real app in CI with provider states (`@State` / `Given`) seeding data, and `pact-broker can-i-deploy --pacticipant X --version <git-sha> --to-environment production` gates the pipeline — plus webhook-triggered provider verification on contract change, bi-directional contracts (verify a provider's OpenAPI against consumer pacts without running the provider), pending/WIP pacts so a new consumer expectation never breaks the provider build, and version pinning via the consumer's git SHA with `record-deployment`/`record-release`.
Debugs a red CI job to root cause instead of blind-rerunning — reproduce locally in the SAME image (`act -j <job>`, `gitlab-runner exec`, `circleci local execute`, or `docker run` the exact pinned digest), read the full log + the real exit code (124=timeout, 137=OOM/SIGKILL, 143=SIGTERM, 139=segfault), then classify into flaky / env-drift / poisoned-or-stale cache / resource-OOM / missing-secret / timeout / test-ordering / network, and confirm with a targeted experiment — diff local-vs-CTRL env (`printenv | sort`, tool `--version`, lockfile hash), run clean (no cache, `--no-cache`/clear key) vs cached, isolate ONE matrix leg, bisect with `git bisect run`, re-run with debug logging (`ACTIONS_STEP_DEBUG=true`, `CI_DEBUG_TRACE=true`, `set -x`) or open an interactive runner (`tmate`/`debug with SSH`/`--privileged` shell) — and fix the cause (pin the digest, scope the cache key, raise the limit, randomize then fix test order) not the symptom.
Diagnoses and fixes non-deterministic test failures at root cause instead of masking them with retries — classify the flake (test-order/shared-state pollution, async timing/sleep races, real-clock/timezone dependence, unseeded RNG, network/IO/external calls, resource leaks, port/temp-dir collisions), reproduce it reliably (loop the test 50–1000×, randomize order with a fixed seed, run in isolation vs full suite to localize), then fix it: inject a fake clock (jest fake timers, `freezegun`, `time-machine`) instead of `Date.now()`, await a condition/`waitFor` instead of `sleep`, seed the RNG and log the seed, isolate state per test (fresh DB transaction-rollback or unique schema/tmpdir per worker, reset globals/singletons in teardown), and pin timezone/locale (`TZ=UTC`, `LC_ALL=C`). Quarantine policy: tag `@flaky`, skip-with-tracking-issue, fix within an SLA, never `retry()` as a permanent fix because retries hide real product races.
Designs paginated list endpoints that stay correct and fast under concurrent writes — cursor/keyset pagination over a stable total ordering with a unique tie-break key (e.g. ORDER BY created_at DESC, id DESC and WHERE (created_at,id) < (?,?)), opaque base64url-encoded cursors that bind sort+filter so they can't be tampered or reused across queries, a sane page_size default (20-50) and hard cap (100), and the {data, next_cursor, has_more} envelope (fetch limit+1 to compute has_more without a COUNT) — instead of OFFSET/LIMIT, which gets O(n) slow on deep pages and skips/duplicates rows when items are inserted or deleted mid-scan; covers REST and GraphQL Relay connections (edges/node/cursor + pageInfo.hasNextPage/endCursor), forward+backward paging, and why total counts are expensive and usually optional.