A production-oriented prompt engineering series, covering prompt structure, context design, few-shot examples, chain-of-thought patterns, prompt injection defense, Prompt CI/CD, version management, A/B testing, automated regression checks, and LLM-as-a-Judge evaluation.
Learn prompt engineering as an evidence-driven discipline rather than a collection of magic phrases. Define task and output contracts, choose zero-shot, few-shot, decomposition, reasoning, or tool-use patterns, separate untrusted context from policy, and evaluate prompt-model bundles with held-out cases, regression gates, latency, cost, and safety checks.
Defend LLM agents from prompt injection with trust boundaries, least privilege, authorization, provenance, egress controls, bound approvals, and adaptive tests.
A practical, provider-neutral guide to context engineering for LLM and Agent systems. Design a context contract, select and retrieve evidence, compress without losing meaning, persist state with provenance and deletion, budget tokens and latency, defend against untrusted content, and evaluate context changes with task-level evidence.
A hands-on companion to context engineering for coding and Agent workflows. Build a bounded task packet, select versioned evidence, maintain durable decisions without treating memory as authority, compress with source links, measure retrieval and cache behavior, and verify permissions, privacy, quality, latency, cost, and rollback.
A practical introduction to prompt injection guardrails for LLM applications: input signals, trusted-data boundaries, least-privilege tools, deterministic authorization, output controls, and security testing.
Detect LLM hallucinations claim by claim. Learn evidence snapshots, four-state verdicts, RAG failure analysis, abstention metrics, and production release gates.
Understand tokenization, context-window budgets, and long-context failure modes without relying on stale model tables or character-per-token rules. This guide explains tokenizer boundaries, input/output reservations, safe truncation, cost reconciliation, chunking, caching, multilingual measurement, and task-level evaluation.
Build reliable LLM tool-calling systems with strict schemas, deterministic dispatch, real-user authorization, idempotency, bounded loops, timeout and retry policy, safe parallelism, untrusted tool-result handling, observability, and evaluation. Explains OpenAI, Anthropic, structured outputs, and MCP boundaries.
Build a maintainable Cursor rules system for a software team. Understand Project, User, Team, and AGENTS.md rules; migrate legacy .cursorrules; keep one source of truth; scope instructions to files and tasks; connect prompts to deterministic permissions and CI; and evaluate changes with replay fixtures, conflict tests, ownership, rollout, and rollback.
Build reliable Cursor and TRAE coding workflows with version-aware rules, explicit file scope, reviewable plans, tests, and rollback. This guide separates provider features from general Prompt practice and avoids unsupported success-rate claims or hard tool promotion.
Use chain-of-thought prompting without treating visible explanations as hidden model reasoning. Compare direct answers, decomposition, few-shot CoT, self-consistency, verifiers, and Tree of Thoughts; then choose a strategy by model, task, accuracy, latency, privacy, and evaluation requirements.
Learn why long-context LLMs miss middle evidence and how to test position, length, distractors, citations, security, latency, and task accuracy reliably.
Design a versioned context architecture for AI coding workflows. This guide separates provider-specific rule files, reusable prompts, MCP tools, retrieval, evaluation, security, precedence, and token budgets without treating any filename as a universal standard.
Learn to govern AGENTS.md as an open repository-instruction format: separate format from host behavior, write scoped commands and boundaries, test discovery and task outcomes, lint files with Go, and secure changes through review.
Design AGENTS.md, CLAUDE.md, and scoped AI coding rules as versioned context artifacts. Govern precedence, trust, tests, security controls, and rollout.
Build Prompt CI/CD around immutable release bundles, offline regression evals, calibrated judges, guarded online experiments, release gates, monitoring, and complete rollback.