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Best resources to learnPrompt Engineering

Study sheet

Prompt engineering is the craft of getting reliable results from large language models. You will learn prompting patterns, structured outputs, evaluation, and the limits of prompting versus fine-tuning or retrieval.

9 resources · · How we curate

🎓Courses(2)

course

Anthropic's Interactive Prompt Engineering Tutorial

Nine notebook chapters with graded exercises: prompt structure, being direct, roles, separating data from instructions, output formatting, step-by-step reasoning, examples, hallucination control, plus chaining and tool use. You write and debug real prompts, not read about them. The single best free way to actually practise rather than read - every chapter ends in an exercise that is graded against expected output, which is the only mechanism on this list that catches the gap between understanding a technique and being able to apply it. Its example code targets an older Claude model, but the failure modes it drills are model-independent.

beginner🇬🇧
course

Anthropic Learn

Anthropic's free learning hub, now Claude Academy, with self-paced courses and tutorials on AI fluency, model capabilities and limits, prompt writing, and building applications with the Claude API. Learners can write clearer prompts, judge model output critically, and start developing with Claude.

beginner✓ Verified Aug 2026🇬🇧

📚Books(1)

bookPaid

Prompt Engineering for LLMs: The Art and Science of Building Large Language Model-Based Applications

Two engineers who built GitHub Copilot explain why prompts work in terms of how models complete text, then build up few-shot patterns, chain-of-thought, retrieval-assembled context and evaluation for production applications rather than one-off chat sessions.

intermediate🇬🇧

🌐Websites(4)

website

Prompt Engineering Guide

Reference covering prompting techniques with worked examples: zero-shot and few-shot, chain-of-thought, self-consistency, ReAct, and tree of thoughts, plus model-specific notes and adversarial prompting risks. Readers finish able to choose techniques deliberately.

intermediate🇬🇧
website

Effective Context Engineering for AI Agents

Anthropic's applied AI team reframes prompting as one part of curating the whole context window. Covers context rot, calibrating system prompt specificity, minimal non-overlapping tool sets, just-in-time retrieval, and compaction, note-taking and sub-agents for long-horizon tasks. This is the resource that answers the topic's hardest question - where prompting stops and retrieval, memory or architecture must take over. It gives concrete mechanisms (compaction, structured note-taking, sub-agent isolation) rather than the vague 'context is the new prompt' takes that flooded blogs afterwards.

intermediate🇬🇧
website

GPT-5.2 Prompting Guide (OpenAI Cookbook)

OpenAI's own guide to steering its current flagship reasoning model: controlling verbosity, preventing scope drift, reasoning-effort settings, long-context handling, tool-call parallelism, schema-driven extraction from documents, and a migration table from earlier models. Reasoning models broke a lot of received prompting wisdom, and this is the clearest first-party account of what changed.

intermediate🇬🇧
website

Prompting Best Practices (Claude Platform Docs)

Anthropic's continuously updated prompting reference, organized as per-model behavioural guidance, then techniques that apply to all current models - clarity, examples, XML structuring, extended thinking, tool use, agentic loops - then migration notes for older prompts.

intermediate🇬🇧

📄Papers(2)

paper

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Shows that prompting a large model with a few worked examples containing intermediate reasoning steps substantially improves arithmetic, commonsense, and symbolic reasoning, and that the effect emerges only at sufficient model scale. The origin of chain-of-thought prompting.

intermediate🇬🇧
paper

The Prompt Report: A Systematic Survey of Prompting Techniques

A 76-page survey of 1,500+ papers that fixes prompting's inconsistent vocabulary and organizes 58 text techniques plus 40 multimodal ones into a single taxonomy. Readers finish able to name, compare and cite techniques precisely. This is the reference map of the whole field - it replaces the endless supply of 'top 20 prompting tricks' posts with one rigorously derived taxonomy and a shared vocabulary. Nothing else in prompt engineering has this level of methodological discipline (PRISMA-style review of 1,500+ papers).

intermediate🇬🇧
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Frequently Asked Questions

What are the best free resources to learn Prompt Engineering?

Dantes has curated 9 resources for Prompt Engineering, including 1 books, 2 courses, 4 websites, 2 papers. All resources are hand-picked for quality — no algorithmic filler. Browse the full list above to find the format that works best for you.

Is Prompt Engineering hard to learn?

Prompt Engineering is approachable at the beginner level — there are resources here specifically for those starting from scratch. As you progress, intermediate and advanced material is also available to take your skills further.

What types of Prompt Engineering learning resources are available on Dantes?

For Prompt Engineering, Dantes has curated 1 books, 2 courses, 4 websites, 2 papers. Each resource type serves a different learning style: videos and YouTube for visual learners, books for depth, courses for structured progression, and websites for quick reference.

How does Dantes select Prompt Engineering resources?

Dantes is an algorithm-free learning directory. Resources are hand-curated based on quality, accuracy, and usefulness — not engagement metrics or paid placements. The goal is to surface the best learning material for Prompt Engineering, whether it's a free YouTube series, a classic textbook, or an open courseware from a top university.

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