If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/AZVktAeFEhAvi Chawla (@_avichawla):Here's a common misconception about RAG!When we talk about RAG, it's usually thought: index the doc → retrieve the same doc.But indexing ≠ retrievalSo the data you index doesn't have to be the data you feed the LLM during generation.Here are 4 smart ways to index data: https://t.co/0nKUuBeJ70 ...

Here are 8 RAG architectures, explained visually: https://t.co/0j9eUVQIfZ ...

Here's a common misconception about RAG!When we talk about RAG, it's usually thought: index the doc → retrieve the same doc.But indexing ≠ retrievalSo the data you index doesn't have to be the data you feed the LLM during generation.Here are 4 smart ways to index data:1) Chunk Indexing- The most common approach.- Split the doc into chunks, embed, and store them in a vector DB.- At query time, the closest chunks are retrieved directly.This is simple and effective, but large or noisy chunks can reduce precisi ...

RT Avi Chawla (@_avichawla)9 MCP, Agents, and RAG projects for AI engineers: https://t.co/fKTuaVMTc9 ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):9 MCP, Agents, and RAG projects for AI engineers: https://t.co/fKTuaVMTc9 ...

Find all these projects in our AI Engineering Hub, along with 90 more hands-on projects: https://t.co/z9IxdiEm8w(don't forget to star it ⭐ ) ...

9 MCP, Agents, and RAG projects for AI engineers: https://t.co/fKTuaVMTc9 ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/AyKdh1l7ajAvi Chawla (@_avichawla):Microsoft.Google.AWS.Everyone's trying to solve the same problem for AI Agents:How to connect your agents to enterprise data without duct-taping a dozen tools together?Your data lives in Postgres, Snowflake, MongoDB, Gmail, etc, scattered across dozens of apps.Your AI https://t.co/huBady60vm ...

MindsDB GitHub repo: https://t.co/PaFtyjuULv(don't forget to star 🌟) ...

Microsoft.Google.AWS.Everyone's trying to solve the same problem for AI Agents:How to connect your agents to enterprise data without duct-taping a dozen tools together?Your data lives in Postgres, Snowflake, MongoDB, Gmail, etc, scattered across dozens of apps.Your AI logic lives in Python scripts and vector databases.Building manual RAG pipelines with custom connectors for every data source means you're already set up for failure.Here's an open-source project tackling this differently:MindsDB treats AI mod ...