FOUNDATIONgeneralAnthropic_Prompt_Engineering_Training_Program.docx
Foundations of Prompt Engineering with Claude
Modules 1–10 of the Anthropic Prompt Engineering Training Program
Master the fundamentals of working with Large Language Models and Anthropic Claude. Learn to design prompts that produce reliable, structured, production-quality outputs across coding, testing, documentation, automation, and enterprise workflows.
Module 1 — Introduction to AI & Anthropic Claude
Where LLMs come from, what makes Claude different, and where it fits in the software development lifecycle.
- 1What are LLMs and Generative AI?A working mental model of Large Language Models: tokens, context windows, temperature, and why prompts matter more than parameters.
- 2Overview of Anthropic ClaudeWho builds Claude, what Constitutional AI means in practice, and why Claude tends to refuse, clarify, and structure where other models drift.
- 3Claude model types and use casesOpus, Sonnet, Haiku — when to pick which. Cost, latency, and capability tradeoffs for production workloads.
- 4AI-assisted software development lifecycleWhere Claude adds the most leverage at every SDLC stage — and where it does not.
Module 2 — Fundamentals of Prompt Engineering
Prompt anatomy, context management, role-based and instruction-based prompting, zero-shot vs few-shot.
- 1What is a prompt?A prompt is not 'the question you ask the model' — it is the complete state the model sees before generating. Treat it like a small program.
- 2Prompt structure and context managementFive-component anatomy: CONTEXT, TASK, CONSTRAINTS, EXAMPLES, OUTPUT. Plus how to manage context windows on long inputs.
- 3Role-based promptingAssigning a role gives the model a stable point of view, vocabulary, and quality bar. Used badly it becomes cosplay; used well it shifts output quality dramatically.
- 4Instruction-based promptingDirect, imperative instructions with measurable success criteria. The most underrated technique.
- 5Few-shot and zero-shot promptingZero-shot is fast but format-fragile. Few-shot examples cost tokens but lock the output shape almost perfectly. When to use each.
Module 3 — Effective Prompt Design
Writing clear and specific prompts, controlling output formats, using examples for accuracy, and iterative prompt optimisation.
- 1Writing clear and specific promptsSpecificity is the highest-leverage edit you can make. A field guide to removing ambiguity.
- 2Controlling AI output formatsMarkdown vs JSON vs XML vs code-only. Pinning structure so downstream code can parse the result reliably.
- 3Using examples for better accuracyHow many examples, where to put them, and how to pick them. Why 'show, do not tell' beats every other technique for format work.
- 4Prompt optimisation techniquesTreat prompts like code. The four-step optimisation loop: baseline, identify failure mode, change one variable, measure.
Module 4 — Claude for Software Development
Generating APIs, schemas, UI, code explanations, debugging, and refactoring with Claude.
- 1Generating APIs and backend servicesPrompt patterns for generating REST/GraphQL APIs, controllers, services, and middleware.
- 2Database schema generationGenerating PostgreSQL/SQL Server/MongoDB schemas with indexes, audit fields, and multi-tenant patterns.
- 3Frontend UI generationGenerating React, Angular, and Vue components and dashboards from descriptions.
- 4Code explanation and debuggingReading unfamiliar code and producing plain-English explanations with red flags.
- 5Refactoring and optimizationReducing duplication, converting sync→async, and tuning hot paths.
Module 5 — Claude for Testing & QA
Generating test cases, API tests, automated suites, performance tests, and bug analysis workflows.
- 1Generating test casesFrom requirements → positive, negative, edge, boundary scenarios.
- 2API testing supportPostman collections, REST/GraphQL validation, auth edge cases.
- 3Automated testing generationSelenium, Playwright, Cypress, xUnit, NUnit, PyTest scaffolds.
- 4Performance testing promptsJMeter and k6 scenarios; bottleneck analysis.
- 5Bug analysis workflowsFrom bug report → reproduction → root cause hypothesis.
Module 6 — Advanced Prompt Engineering
Chain-of-thought, structured JSON outputs, multi-step workflows, prompt templates, AI agent workflows.
- 1Chain-of-thought promptingMake the model reason step-by-step before producing the final answer.
- 2Structured JSON outputsForcing schema-conformant JSON for downstream pipelines.
- 3Multi-step workflow promptsChaining prompts where the output of one becomes the context of the next.
- 4Prompt templatesBuilding reusable, parameterised prompt templates.
- 5AI agent workflowsGoal-directed loops with tool use, planning, and self-critique.
Module 7 — AI Automation & Productivity
Automating documentation, reports, summaries, SOPs, technical docs, and AI-assisted project management.
- 1Automating documentationGenerating READMEs, API docs, deployment guides from source.
- 2Generating reports and summariesDaily standups, weekly reports, release notes from raw activity logs.
- 3Creating SOPs and technical documentsOperational runbooks, incident playbooks, onboarding docs.
- 4AI-assisted project managementJira ticket triage, sprint planning prompts, retrospective synthesis.
Module 8 — Security & Responsible AI Usage
Prompt injection defence, confidential data handling, hallucinations, responsible AI practices.
- 1Avoiding prompt injectionWhat it is, how it works, and defensive patterns (allowlists, output validation, system-prompt isolation).
- 2Handling confidential dataPersonal responsible-AI checklist — what to never paste into a third-party model.
- 3AI limitations and hallucinationsWhere models confidently invent — citations, function signatures, library APIs — and how to detect it.
- 4Responsible AI practicesHuman review gates, traceability, bias awareness, documentation of AI-assisted decisions.
Module 9 — Enterprise AI Integration
Using Claude APIs, integrating into applications, building internal AI tools, workflow orchestration.
- 1Using Claude APIsAnthropic SDK basics: messages API, system prompts, tool use, prompt caching.
- 2Integrating Claude into applicationsServer-side patterns, streaming, retries, rate limit handling, observability.
- 3Building internal AI toolsReference architectures for chatbots, RAG, document search, code review bots.
- 4AI workflow orchestrationMulti-prompt pipelines: triage → enrich → route → respond, with safety gates between stages.
Module 10 — Hands-on Exercises & Projects
Capstone exercises across coding, testing, enterprise workflow, and AI-assisted documentation systems.
- 1Build coding promptsAuthor 5 production-grade coding prompts for tasks your team actually does.
- 2Create testing automation promptsAuthor a prompt that generates a full test suite for a provided service.
- 3Develop enterprise workflow promptsDesign a 3-stage prompt pipeline for an internal workflow (e.g. customer ticket triage → enrichment → response draft).
- 4Design AI-assisted documentation systemsSpecify how an org-wide docs generator should ingest code, infer structure, and produce maintained output.