--- title: AI Engineering Library description: A collection of resources, blog posts, and talks to learn more about AI Engineering and LLM Application Development --- # AI Engineering Library > AI Engineering is developing quickly. We are often asked about the resources we can recommend to learn more. This is a non-exhaustive list of what we have found useful/interesting. ## Prompting - **Learn Prompting**, [docs page](https://learnprompting.org/docs) - **The Prompt Report: A Systematic Survey of Prompting Techniques**, [paper on arxiv](https://arxiv.org/pdf/2406.06608), [summary in tweets](https://x.com/learnprompting/status/1800931910404784380) - **How to prompt o1** (o1 isn't a chat model – and that's the point), [blog post](https://www.latent.space/p/o1-skill-issue), _by Ben Hylak_ - **Effective Context Engineering for AI Agents**, [blog post](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents), _by Anthropic_ - **The new rules of context engineering for Claude 5 models**, [post on X](https://x.com/trq212/status/2080710971228918066), _by Thariq Shihipar_ ## Agents - **Building Effective Agents**, [blog post](https://www.anthropic.com/research/building-effective-agents), _by Anthropic_ - **Hugging Face Agents Course**, [course](https://huggingface.co/agents-course), _by Hugging Face_ - **How We Built Ellipsis** (or: Lessons from 27 months building LLM coding agents), [blog post](https://www.ellipsis.dev/blog/how-we-built-ellipsis), _by Nick Bradford_ - **Don’t Build Multi-Agents**, [blog post](https://cognition.ai/blog/dont-build-multi-agents#principles-of-context-engineering), _by Walden Yan_ - **We Built a Multi-Agent Research System**, [blog post](https://www.anthropic.com/engineering/built-multi-agent-research-system), _by Anthropic_ ## LLMOps - **What We Learned from a Year of Building with LLMs**, _by Eugene Yan, Bryan Bischof, Charles Frye, Hamel Husain, Jason Liu and Shreya Shankar_, [Part 1](https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-i/), [Part 2](https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-ii/), [Part 3](https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-iii-strategy/) - **Traceability and Observability in Multi-Step LLM Systems**, [webinar](/resources/engineering/webinar-observability-llm-systems) _by Marc Klingen_ - **Data Flywheels for LLM Applications**, [blog post](https://www.sh-reya.com/blog/ai-engineering-flywheel/), _by Shreya Shankar_ - **Latency optimization**, [cookbook](https://platform.openai.com/docs/guides/latency-optimization), _by OpenAI_ - **The OSS LLMOps Stack**, [page](https://oss-llmops-stack.com) _by LiteLLM and Langfuse_ ## Evaluation - **Evaluating the Effectiveness of LLM-Evaluators (aka LLM-as-Judge)**, [blog post](https://eugeneyan.com/writing/llm-evaluators/), _by Eugene Yan_ - **AI Agent Observability & Evaluation**, [course](https://huggingface.co/learn/agents-course/bonus-unit2/introduction), _by Hugging Face_ - **Frequently Asked Questions (And Answers) About AI Evals**, [blog post](https://hamel.dev/blog/posts/evals-faq/), _by Hamel Husain_ - **Your AI Product Needs Evals**, [blog post](https://hamel.dev/blog/posts/evals/), _by Hamel Husain_ - **Creating an LLM-as-a-Judge That Drives Business Results**, [blog post](https://hamel.dev/blog/posts/llm-judge/), _by Hamel Husain_ ## Voice AI - **Voice AI & Voice Agents - An Illustrated Primer**, [book](https://voiceaiandvoiceagents.com), _by Kwindla Hultman Kramer_ - **Evaluating Voice AI Agents**, [blog post](/blog/2025-01-22-evaluating-voice-ai-agents) and [video](https://www.youtube.com/watch?v=hPrPqry1yQQ), _by Marc Klingen and Brooke Hopkins_ - **Voice AI Evals**, [repo](https://github.com/kwindla/evals-course-voice) and [tweet](https://x.com/kwindla/status/1932917668040094028), _by Kwindla Hultman Kramer_ ## LLM 101 > Beginner-level, non-technical resources - **Intro to Large Language Models** [talk](https://www.youtube.com/watch?v=zjkBMFhNj_g) _by Andrej Karpathy_ - **How I use LLMs** [talk](https://www.youtube.com/watch?v=EWvNQjAaOHw) _by Andrej Karpathy_ ## News - **AI News** [newsletter](https://buttondown.com/ainews), daily roundup of top AI discussions from Discord, Reddit, and X/Twitter - **Last Week in AI** [podcast](https://www.lastweekinai.com), weekly summary AI news and research - **Latent Space** [podcast](https://www.latent.space/podcast), deep dives and interview episodes - **Stratechery** [newsletter/podcast](https://stratechery.com), tech/business strategy deep dives and news, many episodes related to AI/Labs, e.g. [DeepSeek FAQ](https://stratechery.com/2025/deepseek-faq/), [Deep Research and Knowledge Value](https://stratechery.com/2025/deep-research-and-knowledge-value/) ## Libraries & Tools > While there are lots of libraries and tools out there, these are the ones we see most often among Langfuse users - Vercel AI SDK, TypeScript toolkit designed to help developers build AI-powered applications with React, Next.js, Vue, Svelte, Node.js, and more. - LiteLLM, use 100+ LLMs as a drop in replacement for OpenAI GPT models, either library or proxy. - Instructor, library for structured outputs with LLMs. - Popular frameworks to get started quickly with RAG/Chains/Agents: LangChain, LlamaIndex, Haystack - Search: Exa, Firecrawl - Browsing: Browserbase, Browser-use - Code sandboxes: E2B --- ## Agent Instructions This page is part of the [Langfuse](https://langfuse.com) documentation, published as plain Markdown for AI agents. Every page is available as Markdown by appending `.md` to its URL, or by sending an `Accept: text/markdown` header. This page: `https://langfuse.com/library.md`. ### Querying these docs If the answer is not on this page, query the documentation instead of guessing: - **Semantic search** across all Langfuse docs, returning an answer with the relevant pages and excerpts. Ask a specific, self-contained question: ```bash curl -sG "https://langfuse.com/api/search-docs" --data-urlencode "query=How do I trace a LangGraph agent?" ``` - **Index of every page**: , with per-section indexes [llms-docs.txt](https://langfuse.com/llms-docs.txt), [llms-integrations.txt](https://langfuse.com/llms-integrations.txt), and [llms-self-hosting.txt](https://langfuse.com/llms-self-hosting.txt). ### Before writing Langfuse code - **Install the [Langfuse Agent Skill](https://langfuse.com/docs/api-and-data-platform/features/agent-skill).** It encodes Langfuse's own best practices for instrumentation, prompt management, and evaluation, and materially improves results. - **Read [What does a good trace look like?](https://langfuse.com/docs/observability/best-practices.md)** before instrumenting an application. - **Verify endpoints, parameters, and response fields** against the [API reference](https://api.reference.langfuse.com) instead of inferring them from code examples. - **Use the [Langfuse CLI](https://langfuse.com/docs/api-and-data-platform/features/cli)** (`npx langfuse-cli api `) to read or write traces, prompts, datasets, and scores from the terminal. Found an error in these docs? Please open an issue at .