Build a reliable, layout-preserving PDF transcription pipeline in Python with PyMuPDF and GPT-4o.

Build reliable, stateful AI agents with LangGraph using step-by-step patterns, visual debugging, and persistence.

Build a reliable structured data extraction pipeline using LLMs, LangChain, and OpenAI functions: JSON schemas, deterministic outputs, zero hallucinations, for production.

Avoid hidden Unicode bugs in prompts and RAG by normalizing text, fixing punctuation, and auditing tokenization.

Understand how MCP standardizes tool and data access so your agents interoperate, audit safely, and ship faster, consistently across environments.

Stop losing facts in long LLM prompts. Learn placement rules, query ordering, retrieval tactics to boost accuracy and cut costs.

Quickly choose the right LLM with a practical framework: compare accuracy, context limits, latency, token cost, and risk tolerance today.

Compare speed, accuracy, and cost to decide when SLMs outperform LLMs and how hybrid routing can preserve quality while reducing spend.

Build a semantic cache LLM using embeddings and Redis Vector with TTLs, thresholds, metrics to reduce LLM spend and latency.

Master efficient memory management for large language model serving with PagedAttention to reduce context rot, cut hallucinations, and lower costs.