Prompt Engineering Mastery

Learn prompt engineering with zero-shot, few-shot, Chain-of-Thought, and ReAct patterns. Build structured prompts, evaluate them on held-out tasks, and add practical safety boundaries for production LLM applications.

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.

A version-aware guide to team rules and prompt templates for Cursor. Learn how to separate project conventions from security policy, review generated changes, version scenario prompts, measure failure modes, and evolve shared AI-assisted development guidance without treating instructions as a guarantee.

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 write and test AGENTS.md with scoped commands, repository boundaries, nested files, security controls, linting, and paired coding-agent evaluations.

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.