Topic: ai
50 posts
- 🔗 The RAG Engineer's Guide to Document Parsing
- 🔗 Retrieval Augmented Generation: What It Is and How to Start Using It
- 🔗 Why we no longer use LangChain for building our AI agents
- 🔗 How to scale document question answering using LLMs
- 🔗 Breaking up is hard to do: Chunking in RAG applications
- 🔗 Custom Retriever | 🦜️🔗 LangChain
- 🔗 iyaja/llama-fs: A self-organizing file system with llama 3
- 🔗 Modern Advances in Prompt Engineering
- 🔗 The Problem With LangChain
- 🔗 Developing Rapidly with Generative AI
- 🔗 Lessons after a half-billion GPT tokens
- 🔗 Vector Similarity Search with PostgreSQL's pgvector
- 🔗 Go, Python, Rust, and production AI applications
- 🔗 Generative AI for Contracts
- 🔗 System-wide text summarization using Ollama and AppleScript
- 🔗 Get consistent data from your LLM with JSON Schema
- 🔗 Get Started with Milvus Vector DB in .NET
- 🔗 Q&A with RAG | 🦜️🔗 Langchain
- 🔗 gemini-cli: Access Gemini models from the command-line
- 🔗 Fun With AI Embeddings in Go
- 🔗 Retrieval Augmented Generation in Go
- 🔗 New models and developer products announced at DevDay
- 🔗 Takeaways & lessons from 250k+ LLM calls on 100k corporate docs
- 🔗 Exclusive-OpenAI plans major updates to lure developers with lower costs -sources
- 🔗 Moravec's Paradox