Curated Resources

These are the resources that matter. Not a list — a curated library. Every resource here was selected because it changed how experts think about AI engineering.

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Foundational Papers

Context Engineering

Paper	Authors	Why It Matters
Attention Is All You Need	Vaswani et al. (2017)	The transformer architecture that underlies everything
Lost in the Middle: How Language Models Use Long Contexts	Liu et al. (2023)	The evidence behind the "critical info at top/bottom" rule
REALM: Retrieval-Augmented Language Model Pre-Training	Guu et al. (2020)	Foundational RAG paper
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks	Lewis et al. (2020)	The canonical RAG paper

Agent Engineering

Paper	Authors	Why It Matters
ReAct: Synergizing Reasoning and Acting	Yao et al. (2022)	The ReAct pattern for tool-using agents
Tree of Thoughts	Yao et al. (2023)	Multi-path reasoning for complex problems
Chain of Thought Prompting Elicits Reasoning	Wei et al. (2022)	Evidence for explicit reasoning steps
Reflexion: Language Agents with Verbal Reinforcement Learning	Shinn et al. (2023)	Reflection and self-critique patterns
Plan-and-Solve Prompting	Wang et al. (2023)	Plan before execute for complex tasks

Evaluation

Paper	Authors	Why It Matters
RAGAS: Automated Evaluation of Retrieval Augmented Generation	Es et al. (2023)	Foundation for RAG evaluation
Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena	Zheng et al. (2023)	LLM-as-judge biases and calibration
Large Language Models are not Fair Evaluators	Wang et al. (2023)	Position bias in LLM evaluation

Security

Paper	Authors	Why It Matters
Prompt Injection Attacks Against LLM-Integrated Applications	Greshake et al. (2023)	The first systematic study of prompt injection
Not What You've Signed Up For: Compromising Real-World LLM-Integrated Applications	Greshake et al. (2023)	Indirect prompt injection via tool outputs

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Books

Book	Authors	Why Read It
Designing Machine Learning Systems	Chip Huyen	Systems thinking for ML in production
Building LLM Apps	Various	Practical LLM application patterns
The Pragmatic Programmer	Hunt & Thomas	Still the best engineering mindset book
Release It!	Michael Nygard	Stability patterns for production systems
Accelerate	Forsgren, Humble, Kim	Evidence-based DevOps — applies directly to AI DevOps

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Essential GitHub Repositories

Repository	What It Is	Why Follow It
anthropics/anthropic-cookbook	Claude usage examples	Official Anthropic patterns
openai/openai-cookbook	OpenAI usage examples	Official OpenAI patterns
langchain-ai/langchain	LLM application framework	Most widely used LLM framework
langchain-ai/langgraph	Stateful agent graphs	Production agent orchestration
deepeval-ai/deepeval	LLM evaluation framework	CI/CD evaluation gates
explodinggradients/ragas	RAG evaluation	RAG quality measurement
promptfoo/promptfoo	Prompt testing	Security and comparison testing
microsoft/autogen	Multi-agent framework	Microsoft's agent orchestration
modelcontextprotocol/servers	MCP server library	Reference MCP implementations

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Industry Blogs Worth Following

Blog	Organization	Focus
Anthropic Research	Anthropic	Safety, alignment, Claude capabilities
OpenAI Blog	OpenAI	GPT, agents, DALL-E, safety
Google DeepMind Blog	Google DeepMind	Gemini, research breakthroughs
The Batch	DeepLearning.AI	Weekly AI news digest
Chip Huyen's Blog	Chip Huyen	Practical ML engineering
Lil'Log	Lilian Weng (OpenAI)	Deep technical explanations
Eugene Yan's Blog	Eugene Yan	Applied ML and LLM systems
Simon Willison's Blog	Simon Willison	LLM security, tools, agents

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Standards and Specifications

Standard	Organization	Relevance
EU AI Act	European Union	Mandatory compliance for EU deployments
NIST AI Risk Management Framework	NIST	US AI risk framework
Model Context Protocol Spec	Anthropic	MCP implementation standard
OpenTelemetry Specification	CNCF	Observability standard
Semantic Versioning	Community	Version management for prompts and agents

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Courses

Course	Platform	Who It's For
Short Courses by DeepLearning.AI	DeepLearning.AI	Practical AI engineering, updated frequently
Building Systems with the ChatGPT API	DeepLearning.AI	Production LLM systems
LangChain for LLM Application Development	DeepLearning.AI	LangChain fundamentals
Building and Evaluating Advanced RAG	DeepLearning.AI	Production RAG systems

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Technology Radar

Updated quarterly. Last updated: Q3 2026.

Adopt (Use in production now)

• LangGraph — Stateful agent orchestration
• DeepEval — LLM evaluation in CI/CD
• RAGAS — RAG evaluation
• Langfuse — LLM observability
• MCP — Agent-to-tool integration standard
• PydanticAI — Type-safe LLM interactions

Trial (Evaluate for your use case)

• OpenAI Agents SDK — Native agent orchestration
• Google A2A Protocol — Agent-to-agent communication
• Braintrust — Prompt management and evaluation

Assess (Watch, not yet ready for production)

• Knowledge Graphs + LLMs — GraphRAG patterns maturing
• Mixture of Agents — Multiple models collaborating
• Long context models — 1M+ token windows with reliable recall

Hold (Do not adopt)

• Autonomous agents without human-in-loop for high-stakes tasks — Not ready for production
• LLMs for real-time decisions in regulated industries without human review — Compliance risk