
Akshay π
@akshay_pachaar
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Simplifying LLMs, AI Agents, RAGs and Machine Learning for you! β’ Co-founder @dailydoseofds_β’ BITS Pilani β’ 3 Patents β’ ex-AI Engineer @ LightningAI
Learn AI Engineering π
Joined July 2012
My lecture at MIT!β¨. From Physics to Linear Algebra & Machine learning, I have learned a lot from MIT!. Yesterday, I had the honour of delivering a guest lecture on The state of AI Engineering, exploring:. - Prompt Engineering.- Retrieval Augmented Generation. - Fine-Tuning
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That's a wrap!. If you found it insightful, reshare with your network. Find me β @akshay_pachaar βοΈ.For more insights and tutorials on LLMs, AI Agents, and Machine Learning!.
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If you found it insightful, reshare with your network. Find me β @akshay_pachaar βοΈ.For more insights and tutorials on LLMs, AI Agents, and Machine Learning!.
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Bonus!. We will use @CleanlabAI's AI codex, a smart way to validate and improve your responses. We've used the same for getting the trustworthiness score. Seamlessly integrates with any agentic or AI chat application you're developing. Check this outπ
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4οΈβ£ Indexing & storing. Embeddings created by embedding model are stored in a vector store that offers fast retrieval and similarity search by creating an index over our data. We'll use a self-hosted @Milvusio vector database:
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1οΈβ£ & 2οΈβ£ : Loading the knowledge base. A knowledge base is a collection of relevant and up-to-date information that serves as a foundation for RAG. In our case it's a GitHub repository!. Here's how we chunk & parse our code base using @Llama_Index's hierarchical code parser:
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