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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 advanced LLM chaining patterns including sequential, parallel, conditional, and map-reduce chains. Learn to orchestrate complex AI workflows in production.
Manage long conversations and large documents within LLM context limits using sliding windows, summarization, and map-reduce patterns to avoid the lost-in-the-middle problem.