Llm

Design production-grade AI agents with tool calling, agent loops, parallel execution, human-in-the-loop checkpoints, state persistence, and error recovery.

Build scalable AI background processing with BullMQ, idempotent job tracking, exponential backoff, progress streaming, and webhook callbacks for reliable async workflows.

Master OpenAI JSON Schema, Anthropic tool use, Zod validation, and retry logic for bulletproof LLM data extraction in production.

Decide between fine-tuning and RAG with decision frameworks, cost/performance tradeoffs, hybrid approaches, and evaluation metrics like RAGAS and G-Eval.

Design bulletproof LLM agents with structured tool definitions, parallel execution, result validation, human-in-the-loop gates, and comprehensive observability.

Build resilient LLM APIs with streaming SSE, exponential backoff, model fallback chains, token budgets, prompt caching, and circuit breakers.

Cut LLM costs and latency with exact match caching, semantic caching, embedding similarity, Redis implementation, cost savings, and TTL strategies.

Master token counting, semantic caching, prompt compression, and model routing to dramatically reduce LLM costs while maintaining output quality.

Build comprehensive LLM observability using LangSmith, Langfuse, span instrumentation, and LLM-as-judge evaluations to catch quality regressions and optimize performance.

Implement comprehensive LLM observability with LangSmith/LangFuse integration, token tracking, latency monitoring, cost attribution, quality scoring, and degradation alerts.

Master vision APIs, Whisper transcription, document processing, cost-benefit tradeoffs, and fallback strategies for reliable multimodal AI features.

Defend against prompt injection: direct vs indirect attacks, input sanitization, system prompt isolation, output validation, sandboxed execution, and rate limiting.

Build production-ready RAG systems with semantic chunking, embedding optimization, reranking, citation tracking, and hallucination detection.

Implement production-grade LLM streaming with SSE, OpenAI streaming, backpressure handling, mid-stream errors, content buffering, and abort patterns.