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title: Overview
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sidebar_position: 1
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# Learning Materials
- [Microsoft's LangChain4j for Beginners](https://github.com/microsoft/LangChain4j-for-Beginners)
- [Intro to Large Language Models](https://www.youtube.com/watch?v=zjkBMFhNj_g) by [Andrej Karpathy](https://www.youtube.com/@AndrejKarpathy)
- [Short Courses](https://www.deeplearning.ai/short-courses/) by [DeepLearning.AI](https://www.deeplearning.ai/)
- What We Learned from a Year of Building with LLMs: [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/)
- Book [Understanding LangChain4j](https://agoncal.teachable.com/p/ebook-understanding-langchain4j) by [Antonio Goncalves](https://www.amazon.com/author/agoncal)

# Local LLMs
- [LocalLLaMA community on Reddit](https://www.reddit.com/r/LocalLLaMA/)
- [Ollama](https://ollama.com/)
- [LocalAI](https://localai.io/)
- [Guide to Choosing Quantization Methods and Inference Engines](https://www.reddit.com/r/LocalLLaMA/s/wZ3Sjifnqf)

# Evaluations
- [Your AI Product Needs Evals](https://hamel.dev/blog/posts/evals/)
- [Creating a LLM-as-a-Judge That Drives Business Results](https://hamel.dev/blog/posts/llm-judge/)
- [A Practical Guide to RAG Pipeline Evaluation (Part 1: Retrieval)](https://medium.com/relari/a-practical-guide-to-rag-pipeline-evaluation-part-1-27a472b09893)
- [A Practical Guide to RAG Pipeline Evaluation (Part 2: Generation)](https://medium.com/relari/a-practical-guide-to-rag-evaluation-part-2-generation-c79b1bde0f5d)
- [How important is a Golden Dataset for LLM evaluation?](https://medium.com/relari/how-important-is-a-golden-dataset-for-llm-pipeline-evaluation-4ef6deb14dc5)
- [Case Study: Reference-free vs Reference-based evaluation of RAG pipeline](https://medium.com/relari/case-study-reference-free-vs-reference-based-evaluation-of-rag-pipeline-9a49ef49866c)
- [How to evaluate complex GenAI Apps: a granular approach](https://medium.com/relari/how-to-evaluate-complex-genai-apps-a-granular-approach-0ab929d5b3e2)
- [Generate Synthetic Data to Test LLM Applications](https://medium.com/relari/generate-synthetic-data-to-test-llm-applications-4bffeb51b80e)

# Agents
[Building effective agents](https://www.anthropic.com/research/building-effective-agents) by Anthropic

# Leaderboards

## Language Models
- [LMSYS Chatbot Arena](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard)
- [SEAL Leaderboards](https://scale.com/leaderboard)
- [Comparing models for quality, speed, price, etc.](https://artificialanalysis.ai/)
- Hallucinations: [Vectara](https://huggingface.co/spaces/vectara/leaderboard), [Hallucinations](https://huggingface.co/spaces/hallucinations-leaderboard/leaderboard)
- Code Generation: [BigCode](https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard)
- Tools/Functions: [Gorilla](https://gorilla.cs.berkeley.edu/leaderboard.html), [Nexus](https://huggingface.co/spaces/Nexusflow/Nexus_Function_Calling_Leaderboard), [Toolbench](https://huggingface.co/spaces/qiantong-xu/toolbench-leaderboard)
- [Performance](https://huggingface.co/spaces/optimum/llm-perf-leaderboard) (latency, throughput, memory, etc.)
- [Enterprise Scenarios](https://huggingface.co/spaces/PatronusAI/enterprise_scenarios_leaderboard)

## Embedding Models
- [MTEB](https://huggingface.co/spaces/mteb/leaderboard)

## More Leaderboards
- [All leaderboards on HuggingFace Spaces](https://huggingface.co/spaces?sort=likes&search=leaderboard)
