23h • General discussion
⚡RAG is Necessary & how it solves Real-World AI Limitations
The Large Language Models are incredible, but they aren't perfect. They have knowledge cutoffs, they lack citations, they sometimes hallucinate, and they are restricted by context windows.
So, how do we fix this? Retrieval Augmented Generation (RAG). 🧠
We just had an absolute masterclass on RAG and LlamaIndex led by Mohammad Arshad sir in the Decoding Data Science AI_Residency Program!
This wasn't just theory; we got hands-on with the Llama Index framework. It was fascinating to see how we could build a RAG system in just five lines of code to load documents, create an index, and generate grounded responses based on our private data, not just the model's memory.
💪 My biggest takeaways:
➡️ We tackled performance optimization. By saving vector stores to local persistent storage (rather than re-indexing every time), we watched our query times drop from around 2 seconds to less than 1 second! ⚡
➡️ Treating the LLM as an inference engine and utilizing specialized vector databases for semantic retrieval is the key to building reliable, enterprise-ready AI.
➡️ This is how we reduce AI hallucinations and build reliable systems.
I’ve got some homework to do analyzing Llama Index dependencies and storage outputs, but I am incredibly excited for the next session where we tackle an vector embeddings and preparing for our upcoming sessions on real-world enterprise chatbots.🤖
📌 During this session, I built a RAG with LlamaIndex simple AI bot; here is the link-
👆 Ask questions about the related to documents loaded into the system.
Try & test this - give your valuable feedback. If good, give your like!
#RAG #LlamaIndex #GenerativeAI #AIResidencyCohort10 #DataScience #MachineLearning #LlamaIndex #VectorSearch #DecodingDataScience #ArtificialIntelligence #AiResident
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⚡RAG is Necessary & how it solves Real-World AI Limitations
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