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A practitioner's guide to learning resources for building, deploying, evaluating, and governing AI systems. Organized by what you need to learn and why.
This guide answers three questions for every resource:
- What skill or knowledge does this build?
- Who needs this in a serious organization?
- How does it connect to working with frontier AI systems?
Note: For tools and software, see AI Tools. This page focuses on learning resources.
Contents
Learning Paths by Role
Where to start based on what you're trying to accomplish.
| Role |
Core Skills Needed |
Start Here |
| AI Product Manager |
Prompt design, evaluation, user research, cost modeling |
Prompt Engineering → Evaluation |
| Software Engineer (adding AI) |
API integration, prompt engineering, error handling |
LLM Foundations → Prompt Engineering |
| AI/ML Engineer |
Agent architecture, RAG systems, evaluation, deployment |
Agent Development → RAG |
| Data Scientist |
Fine-tuning, embeddings, evaluation metrics |
LLM Foundations → Evaluation |
| Security Engineer |
Prompt injection, guardrails, red teaming |
AI Safety → Evaluation |
| Compliance/Legal |
AI governance frameworks, risk assessment, audit |
AI Governance |
| Executive/Decision Maker |
AI strategy, risk, organizational change |
AI Governance → AI for Everyone course |
| Researcher |
Architecture, training, alignment theory |
Landmark Papers → Classical ML |
Prompt Engineering & Context Design
The core skill for working with LLMs. How to communicate effectively with AI systems.
Courses & Tutorials
| Resource |
What You'll Learn |
Who It's For |
Link |
| Anthropic Prompt Engineering Guide |
Claude-specific best practices, system prompts, XML tags |
Developers using Claude |
docs.anthropic.com |
| OpenAI Prompt Engineering Guide |
GPT best practices, few-shot, chain-of-thought |
Developers using OpenAI |
platform.openai.com/docs |
| Prompt Engineering Guide (DAIR.AI) |
Comprehensive techniques across models |
All practitioners |
promptingguide.ai |
| Learn Prompting |
Structured course from basics to advanced |
Beginners to intermediate |
learnprompting.org |
| DeepLearning.AI: ChatGPT Prompt Engineering |
Practical prompt engineering with Andrew Ng |
Developers, data scientists |
deeplearning.ai |
| Google Prompt Design Guide |
Gemini-specific prompting strategies |
Developers using Google AI |
ai.google.dev |
Key Reading
| Resource |
What You'll Learn |
Link |
| "Prompt Engineering" (Lilian Weng) |
Deep technical overview of prompting techniques |
lilianweng.github.io |
| "What We Learned from a Year of Building with LLMs" |
Production lessons from practitioners |
oreilly.com |
| Anthropic's Claude Character |
How Claude is designed to behave and why |
anthropic.com |
Agent Development & Orchestration
Building AI systems that reason, plan, and take actions. The frontier of applied AI.
Courses & Tutorials
| Resource |
What You'll Learn |
Who It's For |
Link |
| DeepLearning.AI: AI Agents in LangGraph |
Multi-step agents with state and cycles |
AI engineers |
deeplearning.ai |
| DeepLearning.AI: Multi AI Agent Systems with CrewAI |
Role-based multi-agent collaboration |
Teams building agent teams |
deeplearning.ai |
| LangChain Academy |
Comprehensive LangChain/LangGraph training |
LangChain users |
academy.langchain.com |
| Anthropic MCP Documentation |
Building tool-using agents with Model Context Protocol |
Agent developers |
modelcontextprotocol.io |
| AutoGen Tutorial |
Microsoft's multi-agent framework |
Enterprise teams |
microsoft.github.io/autogen |
| OpenAI Function Calling Guide |
Tool use and structured outputs |
OpenAI API users |
platform.openai.com |
Key Reading
| Resource |
What You'll Learn |
Link |
| "The Shift from Models to Compound AI Systems" |
Why agents and pipelines matter more than models |
bair.berkeley.edu |
| "Building Effective Agents" (Anthropic) |
Practical patterns for agent development |
anthropic.com |
| "Cognitive Architectures for Language Agents" |
Academic framework for agent design |
arxiv.org |
| LangGraph Conceptual Guide |
When and how to use agentic patterns |
langchain-ai.github.io |
RAG & Knowledge Systems
Connecting AI to your organization's data. Essential for enterprise AI.
Courses & Tutorials
| Resource |
What You'll Learn |
Who It's For |
Link |
| DeepLearning.AI: Building and Evaluating Advanced RAG |
Production RAG with evaluation |
ML engineers |
deeplearning.ai |
| LlamaIndex Documentation |
Data connectors, indexing, retrieval |
RAG developers |
docs.llamaindex.ai |
| Pinecone Learning Center |
Vector search fundamentals and best practices |
Teams implementing RAG |
pinecone.io/learn |
| DeepLearning.AI: Vector Databases |
Embeddings and similarity search |
Data engineers |
deeplearning.ai |
| Weaviate Academy |
Hands-on vector database training |
Weaviate users |
weaviate.io/developers/academy |
Key Reading
| Resource |
What You'll Learn |
Link |
| "Patterns for Building LLM-based Systems" |
RAG, agents, and production patterns |
eugeneyan.com |
| "Chunking Strategies for LLM Applications" |
How to split documents for retrieval |
pinecone.io |
| "A Survey on RAG for LLMs" |
Comprehensive academic overview |
arxiv.org |
| "Retrieval Augmented Generation: A Practical Guide" |
End-to-end RAG implementation |
docs.cohere.com |
Evaluation & Testing
Measuring AI quality. The difference between demos and production systems.
Courses & Tutorials
| Resource |
What You'll Learn |
Who It's For |
Link |
| DeepLearning.AI: Evaluating and Debugging Generative AI |
Systematic evaluation methods |
ML engineers |
deeplearning.ai |
| Promptfoo Documentation |
Automated prompt testing and red teaming |
Developers testing prompts |
promptfoo.dev/docs |
| Inspect AI Documentation |
UK AISI's evaluation framework |
Safety researchers, evaluators |
inspect.ai-safety-institute.org.uk |
| Ragas Documentation |
RAG-specific evaluation metrics |
RAG developers |
docs.ragas.io |
| Braintrust Documentation |
Building evaluation pipelines |
Production ML teams |
braintrust.dev/docs |
Key Reading
| Resource |
What You'll Learn |
Link |
| "Your AI Product Needs Evals" |
Why and how to evaluate LLM applications |
hamel.dev |
| "How to Evaluate LLMs" |
Practical evaluation strategies |
oreilly.com |
| "LLM-as-Judge" |
Using LLMs to evaluate LLM outputs |
arxiv.org |
| Anthropic's Evaluation Documentation |
How Anthropic thinks about evals |
docs.anthropic.com |
AI Safety & Alignment
Building AI systems that are robust, secure, and aligned with human values.
Courses & Tutorials
| Resource |
What You'll Learn |
Who It's For |
Link |
| AISF Fundamentals Course |
AI safety foundations |
Researchers, engineers |
aisafetyfundamentals.com |
| DeepLearning.AI: Red Teaming LLM Applications |
Adversarial testing techniques |
Security engineers |
deeplearning.ai |
| Center for AI Safety Course |
Technical AI safety concepts |
Researchers |
course.mlsafety.org |
| Guardrails AI Documentation |
Implementing input/output validation |
Developers adding safety |
guardrailsai.com/docs |
| NeMo Guardrails Guide |
NVIDIA's conversational safety toolkit |
Chatbot developers |
docs.nvidia.com |
Key Reading
| Resource |
What You'll Learn |
Link |
| Anthropic Core Views on AI Safety |
How Anthropic approaches safety |
anthropic.com |
| "Constitutional AI" (Anthropic) |
Self-correction against principles |
anthropic.com |
| "Prompt Injection Primer" |
Understanding prompt injection attacks |
simonwillison.net |
| OWASP Top 10 for LLM Applications |
Security risks in LLM systems |
owasp.org |
| "Sleeper Agents" |
Research on deceptive AI behavior |
arxiv.org |
| UK AISI Evaluations |
Government AI safety assessment approach |
aisi.gov.uk |
AI Governance & Ethics
Managing AI at the organizational level. Policy, risk, compliance, and accountability.
Courses & Tutorials
| Resource |
What You'll Learn |
Who It's For |
Link |
| AI for Everyone (Andrew Ng) |
Non-technical AI literacy for leaders |
Executives, managers |
coursera.org |
| Responsible AI (Google) |
Principles for ethical AI development |
All practitioners |
ai.google/responsibility |
| Microsoft Responsible AI |
Enterprise responsible AI framework |
Enterprise teams |
microsoft.com/ai/responsible-ai |
| AI Ethics (MIT) |
Technical and philosophical foundations |
Researchers, policy makers |
mitsloan.mit.edu |
Key Reading
| Resource |
What You'll Learn |
Link |
| NIST AI Risk Management Framework |
US government AI risk framework |
nist.gov |
| EU AI Act Summary |
European AI regulation overview |
artificialintelligenceact.eu |
| "Model Cards for Model Reporting" |
Standardized model documentation |
arxiv.org |
| Anthropic's Responsible Scaling Policy |
How to scale AI development responsibly |
anthropic.com |
| "On the Dangers of Stochastic Parrots" |
Critical perspective on large language models |
dl.acm.org |
LLM Foundations
Understanding how large language models work. Essential context for practitioners.
Courses & Tutorials
| Resource |
What You'll Learn |
Who It's For |
Link |
| Andrej Karpathy: Neural Networks - Zero to Hero |
Build GPT from scratch |
Engineers wanting deep understanding |
youtube.com |
| Stanford CS324: Large Language Models |
Academic LLM foundations |
Researchers, advanced engineers |
stanford-cs324.github.io |
| Hugging Face NLP Course |
Transformers, fine-tuning, deployment |
ML engineers |
huggingface.co/course |
| DeepLearning.AI: Generative AI with LLMs |
LLM architecture, training, fine-tuning |
ML practitioners |
coursera.org |
| Full Stack LLM Bootcamp |
End-to-end LLM application development |
Full-stack developers |
fullstackdeeplearning.com |
| LLM University (Cohere) |
Embeddings, RAG, fine-tuning fundamentals |
All practitioners |
cohere.com/llmu |
Key Reading
| Resource |
What You'll Learn |
Link |
| "The Illustrated Transformer" |
Visual guide to transformer architecture |
jalammar.github.io |
| "Attention Is All You Need" Annotated |
The foundational paper, explained |
nlp.seas.harvard.edu |
| "A Survey of Large Language Models" |
Comprehensive LLM overview |
arxiv.org |
| State of GPT (Andrej Karpathy) |
How GPT models are trained and used |
youtube.com |
Classical ML & Deep Learning
Foundational knowledge. Still relevant for understanding and when LLMs aren't the right tool.
Courses
| Resource |
What You'll Learn |
Who It's For |
Link |
| Machine Learning (Andrew Ng) |
ML fundamentals: regression, classification, neural nets |
Beginners |
coursera.org |
| Deep Learning Specialization |
Neural networks, CNNs, RNNs, sequence models |
Engineers building models |
coursera.org |
| Fast.ai Practical Deep Learning |
Hands-on deep learning with code |
Practitioners wanting fast results |
fast.ai |
| CS231n: CNNs for Visual Recognition |
Computer vision foundations |
Vision ML engineers |
cs231n.stanford.edu |
| CS224n: NLP with Deep Learning |
NLP foundations (pre-LLM techniques still useful) |
NLP engineers |
web.stanford.edu/class/cs224n |
| Elements of AI |
Non-technical AI introduction |
Everyone |
elementsofai.com |
| StatQuest (Josh Starmer) |
Intuitive statistics and ML explanations |
Visual learners |
youtube.com/statquest |
Books
| Book |
What You'll Learn |
Who It's For |
Link |
| Deep Learning (Goodfellow et al.) |
Comprehensive deep learning theory |
Researchers, advanced practitioners |
deeplearningbook.org |
| Hands-On Machine Learning (Géron) |
Practical ML with scikit-learn, Keras, TensorFlow |
Practitioners |
oreilly.com |
| The Hundred-Page Machine Learning Book |
Concise ML overview |
Everyone needing quick reference |
themlbook.com |
| Designing Machine Learning Systems (Huyen) |
Production ML system design |
ML engineers |
oreilly.com |
| AI Engineering (Huyen) |
Building AI products and systems |
AI/ML engineers |
oreilly.com |
| Build a Large Language Model (From Scratch) |
Implement an LLM step by step |
Engineers wanting deep understanding |
manning.com |
Landmark Research Papers
Essential papers for understanding how we got here and where we're going.
Foundational (Pre-2020)
| Paper |
Year |
Significance |
Link |
| Attention Is All You Need |
2017 |
Introduced the Transformer architecture |
arxiv.org |
| BERT |
2018 |
Bidirectional pre-training for NLP |
arxiv.org |
| GPT-2 (Language Models are Unsupervised Multitask Learners) |
2019 |
Scaling and emergence in language models |
openai.com |
| ImageNet Classification with Deep CNNs (AlexNet) |
2012 |
Launched the deep learning revolution |
papers.nips.cc |
| Deep Residual Learning (ResNet) |
2016 |
Enabled very deep networks |
arxiv.org |
| Generative Adversarial Networks |
2014 |
Generative modeling breakthrough |
arxiv.org |
| Playing Atari with Deep RL |
2013 |
Deep reinforcement learning |
arxiv.org |
LLM Era (2020-2023)
| Paper |
Year |
Significance |
Link |
| GPT-3 (Language Models are Few-Shot Learners) |
2020 |
In-context learning at scale |
arxiv.org |
| Scaling Laws for Neural Language Models |
2020 |
Predictable scaling behavior |
arxiv.org |
| Training Compute-Optimal LLMs (Chinchilla) |
2022 |
Optimal data/compute tradeoffs |
arxiv.org |
| Chain-of-Thought Prompting |
2022 |
Reasoning through intermediate steps |
arxiv.org |
| Constitutional AI |
2022 |
AI self-improvement with principles |
arxiv.org |
| RLHF (Training Language Models to Follow Instructions) |
2022 |
Human feedback for alignment |
arxiv.org |
| LLaMA |
2023 |
Open-weight foundation models |
arxiv.org |
| GPT-4 Technical Report |
2023 |
Multimodal frontier model |
arxiv.org |
| Retrieval-Augmented Generation (RAG) |
2020 |
Grounding LLMs with retrieval |
arxiv.org |
| LoRA: Low-Rank Adaptation |
2021 |
Efficient fine-tuning |
arxiv.org |
Agents & Reasoning (2023-2025)
| Paper |
Year |
Significance |
Link |
| ReAct: Reasoning and Acting in LLMs |
2023 |
Foundation for LLM agents |
arxiv.org |
| Toolformer |
2023 |
LLMs learning to use tools |
arxiv.org |
| Tree of Thoughts |
2023 |
Structured reasoning exploration |
arxiv.org |
| Self-Consistency |
2023 |
Multiple reasoning paths for reliability |
arxiv.org |
| Voyager: Minecraft Agent |
2023 |
Lifelong learning agent |
arxiv.org |
| DSPy |
2023 |
Programming (not prompting) LLMs |
arxiv.org |
| GAIA Benchmark |
2023 |
Evaluating general AI assistants |
arxiv.org |
| Let's Verify Step by Step |
2023 |
Process reward models for reasoning |
arxiv.org |
| The Claude 3 Model Family |
2024 |
Frontier model capabilities and safety |
anthropic.com |
Safety & Alignment
| Paper |
Year |
Significance |
Link |
| Concrete Problems in AI Safety |
2016 |
Foundational safety research agenda |
arxiv.org |
| Scaling Monosemanticity |
2023 |
Interpreting neural network features |
anthropic.com |
| Sleeper Agents |
2024 |
Deceptive behavior in AI systems |
arxiv.org |
| Many-Shot Jailbreaking |
2024 |
Long-context safety vulnerabilities |
anthropic.com |
| Towards Monosemanticity |
2023 |
Understanding neural network internals |
anthropic.com |
Communities & Discussion
Where practitioners share knowledge, debug problems, and stay current.
Technical Communities
Professional Networks
| Community |
Focus |
Who It's For |
Link |
| AI Engineer Foundation |
Applied AI engineering |
Professional AI engineers |
ai.engineer |
| Weights & Biases Community |
ML experiment tracking |
W&B users |
wandb.ai/community |
| dbt Community |
Data transformation (AI-adjacent) |
Data engineers |
getdbt.com/community |
Podcasts & Video Channels
Stay current with developments and learn from practitioners.
Podcasts
YouTube Channels
Conferences & Events
Where the field advances and practitioners connect.
Major Research Conferences
Applied AI Events
Newsletters & Blogs
Curated updates and analysis.
Newsletters
Company Research Blogs
Individual Blogs
The Integration Challenge
Companies will not struggle to access AI.
They will struggle to integrate, trust, measure, and govern it under pressure.
This is why resources on Evaluation, Safety, and Governance matter as much as resources on building. The practitioners who succeed will be those who invest in:
- Systematic evaluation before deployment
- Safety engineering as a core competency
- Governance frameworks that scale with AI adoption
- Continuous learning as the field evolves rapidly
Notes
Feedback and suggestions are welcome!
This list is maintained as part of the Awesome Prompt Engineering collection. For tools and software, see AI Tools.
Last updated: January 2026