Introduction to LLM

This page provides an easy-to-understand guide on LLMs (Large Language Models) from basics to applications for AI enthusiasts.

Total of 15 articles available. | Currently on page 1 of 1.

Chapter 14 — Benchmarking, Testing, and Performance

Fifteenth and final post of the LLM Primer IV walkthrough. The MCP-Universe Benchmark on real servers, the two systemic failure modes it exposed, the ten-times throughput gap between session-per-request and shared session pools, and the bridge to Volume V.

2026-04-12

Chapter 13 — Frameworks and Cloud Integration

Fourteenth post of the LLM Primer IV walkthrough. Strands with Bedrock, the AWS state-layer pattern, the Microsoft Agent Framework, LangChain, Semantic Kernel — and the three production integration shapes teams keep arriving at independently.

2026-04-11

Chapter 7 — Advanced Collaborative and Dynamic Patterns

Seventh post of the LLM Primer IV walkthrough. Roundtable consensus, handoff routing, and magentic orchestration — the patterns that emerge when the topology has to be built per request, with the failure modes (non-termination, mis-routing, runaway planning) the simpler patterns avoid.

2026-04-05

Chapter 1 — The AI Integration Crisis and the Rise of Agentic Architecture

First post of the LLM Primer IV walkthrough. Why monolithic agents fray as system prompts grow, the N times M integration problem hiding underneath, and the move from prompt engineering to context engineering that MCP was built to enable.

2026-03-30

Chapter 11 — Continuous Updates and Pipeline Optimization

Eleventh and final post of the LLM Primer III walkthrough. CDC and incremental indexing keep the corpus fresh, semantic caching and model tiering keep latency down, and a four-stage feedback loop closes the gap between what production tells the team and what the team actually changes — plus a bridge to Volume IV on Model Context Protocol.

2026-03-28

Chapter 10 — Leading Evaluation Frameworks

Tenth post of the LLM Primer III walkthrough. A field guide to the frameworks that turn the Evaluation Triad into something a team can actually run — RAGAS, TruLens, DeepEval on one side, Braintrust, LangSmith, Phoenix, Galileo, Opik on the other, and the Evaluation Gap none of them has yet closed.

2026-03-27

Chapter 11 — Evaluation, Calibration, and Inference

Eleventh post of the LLM Primer II walkthrough. Perplexity, calibration, the error bars that every benchmark score should carry, and the mathematics of measuring hallucination — the chapter where we ask how anyone can measure a machine that can say anything.

2026-03-13

Chapter 10 — Post-Training and Alignment Mathematics

Tenth post of the LLM Primer II walkthrough. The mathematics that civilizes a brilliant but feral next-word predictor into a helpful assistant — supervised fine-tuning, reward modeling, RLHF on a KL leash, and the elegant DPO derivation that collapses the whole pipeline into a single supervised loss.

2026-03-12

Chapter 8 — Using LLMs in Applications: Chatbots, Code, Extraction, and Agents

Chapter 8 of the LLM Primer I series. The application patterns that actually ship in production — chatbots, summarization, code assistants, structured extraction, and the rise of agentic systems where the model drives a tool-use loop. Plus the benchmarks every engineer should recognize by name.

2026-02-25

Chapter 6 — Fine-Tuning & Adaptation: From Raw Model to Helpful Assistant

Chapter 6 of the LLM Primer I series. The full adaptation stack — from cheap prompt-based steering to parameter-efficient fine-tuning to full alignment with RLHF and its modern successors like DPO. Why post-training is now where closed-model APIs actually differentiate.

2026-02-23