llm

Make loops trustworthy. Adversarial verification panels, sub-agent orchestration, loop-until-dry for unbounded discovery, loop-until-budget for paid depth, multi-modal sweeps with diverse prompts, eval-driven cap selection, and the five anti-patterns — silent caps, infinite plans, drift, premature termination, thrashing — that ship to prod more than they should.

Once an agent loop runs past a few iterations the failure surface shifts: state bloats, tool calls fail in three distinct ways, retries multiply if you stack them wrong, and unbounded budgets rack up four-figure bills overnight. The state, errors, and budgets you need to make the loop survive contact with reality.

How LLM agents actually run: the iterative prompt-action-observation loop, the ReAct shape, the smallest tool-calling loop in twelve lines, why you always stack three termination layers, what the model sees on iteration N, and when a single prompt is the better answer.

Master the OTel GenAI semantic conventions — gen_ai.* attributes, span structure for prompts/completions/tools, sampling strategies, and cost attribution — and understand why standardizing across LangSmith, Phoenix, Datadog, and Grafana matters for production AI systems.

Learn how to systematically evaluate Retrieval-Augmented Generation systems in production using RAGAS, TruLens, and Phoenix — covering golden sets, retrieval drift, embedding drift, and cost-aware eval scheduling.

A deep look at how reinforcement learning on chains-of-thought powers o1, DeepSeek-R1, Claude reasoning, and Gemini Thinking — covering GRPO, MCTS-style search, test-time compute scaling, and distillation into smaller models.

A deep dive into DPO (Rafailov et al. 2023) and its successors — how they reformulate RLHF as a classification problem, the math behind the implicit reward, and where each variant wins or loses against PPO-based pipelines.

An honest map of RL in 2026 — the domains where it actually reaches production (LLM post-training, robotics policies, ad bidding, RLHF, reasoning models) and the places where it still cannot reliably cross the lab-to-deployment gap.

A deep dive into how large language models actually run in production — why prefill is fast and decode is slow, how the KV cache works, sampling strategies like temperature and top-p, speculative decoding, and continuous batching with vLLM.

A deep dive into how raw pretrained language models become helpful assistants — from supervised fine-tuning on curated demonstrations, through reward modeling and PPO-based RLHF, to modern direct alignment methods like DPO and the recipes used in Llama 3, Llama 4, and DeepSeek.

How two landmark papers — Kaplan et al. 2020 and DeepMind's Chinchilla 2022 — rewrote our understanding of compute-optimal training, why the industry now deliberately overtrains models, and how inference costs flip the math entirely.

A deep tour of the architectural choices powering today's large language models — decoder-only Transformers, encoder-decoder designs, grouped-query attention, RoPE positional embeddings, and mixture-of-experts routing — with concrete numbers and trade-offs.

A deep dive into how large language models learn from raw text: the next-token prediction objective, cross-entropy loss, the messy reality of web data curation (Common Crawl, dedup, quality filters), and the lineage from The Pile to FineWeb.

How to manage, compress, and strategically fill the context window in long-horizon agents — covering summarization checkpoints, scratchpad memory, retrieval injection, prompt caching, and compaction triggers.

A deep dive into designing tool schemas that LLMs actually call correctly — covering parameter naming, description quality, structured output via response schemas, output parsing, and error message design that drives self-correction.

Move beyond toy ReAct loops. Learn how to build a production-grade agent harness with a robust control loop, tool registry, schema validation, retry logic, token budgets, abort signals, and a persistent journal that survives crashes.

A deep dive into MCP — Anthropic's open protocol for connecting LLMs to external tools, data, and prompts. Covers JSON-RPC transport layers, the three core primitives, capability negotiation, and how to build a working server from scratch.

A rigorous guide to measuring RAG quality using RAGAS metrics — faithfulness, answer relevancy, context precision, and context recall — plus how to build a golden dataset and recognize where automated metrics fall short.

Go beyond naive RAG pipelines. Learn how Self-RAG, Corrective RAG, and retrieval-as-tool patterns let an LLM decide when, what, and how many times to retrieve — enabling reliable multi-hop reasoning over complex knowledge bases.

Deep dive into two advanced RAG architectures — HippoRAG's hippocampal-inspired knowledge graph indexing and RAPTOR's recursive summarization tree — and why both dramatically outperform flat vector retrieval on multi-hop questions.

Go beyond dense-vector search: learn how Microsoft GraphRAG extracts entities, builds a knowledge graph, clusters it with the Leiden algorithm, and serves both local and global queries with community summaries — delivering answers that classic RAG cannot.

Master four advanced query rewriting techniques that dramatically improve RAG retrieval quality: Hypothetical Document Embeddings, multi-query expansion, query decomposition, and step-back prompting. Learn when to reach for each and how to implement them.

A deep dive into how you split documents for retrieval-augmented generation — fixed-size, recursive, semantic, hierarchical, and late chunking — with concrete trade-offs and code for each approach.

You already let Copilot autocomplete your code. This lesson dives into the underused power features: chat participants, slash commands, Copilot Edits, agent mode, custom instructions, prompt files, the model picker, MCP servers, and Copilot Spaces.

A field guide to the LLM benchmarks practitioners cite in 2026 — what each one measures, where it's saturated, where contamination risk is high, and why benchmark gains rarely transfer to your task without your own eval.

How frontier LLM costs and latency actually work in 2026 — input vs output token asymmetry, prompt caching, batch API discounts, TTFT and tokens-per-second, reasoning-model amplification, and when self-hosting breaks even.

A practical decision framework for picking an LLM in 2026 — define the task, build an offline eval, measure quality + latency + cost on real candidates, and avoid the classic trap of optimizing for benchmarks instead of your task.

An honest look at the open-weights vs closed-API choice for LLMs in 2026 — covering data privacy, cost at scale, fine-tuning, latency, regulatory concerns, and the gap in raw capability per dollar.

A capability-by-capability tour of frontier LLMs in 2026 — which models are strong at reasoning, code, math, long-context, multilingual, multimodal, and tool use, with hedged comparisons instead of point-estimate benchmark wars.

A practitioner's map of the major frontier-model labs as of early 2026 — who they are, what they ship, which models are open vs closed, and how the field has split into general-purpose chat and reasoning families.

Why LLM apps are uniquely vulnerable to attacks delivered as plain text, the difference between direct and indirect injection, and the defences that actually help (plus the ones that don't).

What "multimodal" actually means once you get past marketing copy. How modern models like GPT-4o, Gemini, and Claude blend modalities, and the design trade-offs (early vs late fusion, native vs adapted) you'll meet when building with them.

What separates an agent from a plain chatbot, the perceive-think-act loop they all share, and how to design one that doesn't loop forever or burn through your token budget.

Explore Retrieval-Augmented Generation (RAG), a powerful technique that enhances Large Language Models (LLMs) by grounding their responses in external, up-to-date, and domain-specific information, mitigating hallucinations and improving factual accuracy. This lesson covers its core components, workflow, and practical considerations.

A beginner's guide to LangChain, the popular framework for composing applications with Large Language Models. Learn the core concepts of Models, Prompts, and Chains, and build a simple application using the LangChain Expression Language (LCEL).

Dive into LangSmith, the developer platform for building and evaluating robust Large Language Model (LLM) applications. Learn how to trace execution paths, debug complex chains, and rigorously evaluate your LLM's performance to ensure reliability and quality.