Learn how to build a semantic search and a Q/A chat assistant that lets you ask questions about your podcast transcripts to gain further insights. This is especially useful if you want to save time by avoiding the need to go back and forth through each of the videos.

This blog explores LLMOps, focusing on integrating LLMs into business workflows. It addresses key challenges such as handling unstructured data and ensuring output accuracy and offers insights into maintaining the reliability and effectiveness of LLMs.

This is my story of building KinConnect, a tool designed to help hackathon participants form effective teams using AI-driven participant profiles and matching algorithms. The project was developed during the MongoDB GenAI Hackathon using tools like Google Forms, Pipedream, FireworksAI, Modal Labs, and MongoDB Hybrid Search. Some lessons learnt are the importance of experimentation, prompt engineering, and leveraging synthetic data.

AWS introduces Meta Llama 3 models on Trainium and Inferentia in SageMaker JumpStart, reducing deployment costs by up to 50%. This enables scalable, customizable deployments using a no-code approach or the SageMaker JumpStart SDK.

While robust test coverage is critical for high-quality products, the non-deterministic nature and complexity of LLMs often render traditional testing methods inadequate. In addition to LLM evaluation strategies like gold data benchmarks, cross-model evaluations, and probabilistic assertions, this article discusses scenarios where mocking is beneficial. It also provides concrete examples of different mocking techniques to ensure robust testing. By strategically applying these methods, development teams can enhance productivity, control costs, and ensure the reliability of LLM applications in production.

This article provides a guide on creating an LLM application using LlamaIndex and Qdrant to interact with GitHub repositories for easy retrieval of code snippets. The application is deployed on Google Kubernetes Engine with Docker and FastAPI and includes a user-friendly Streamlit interface for sending queries. You can code along following the repository and the instructions step by step.

This blog post explores the process of building and deploying a serverless LLM application to perform semantic searches over academic papers using AWS Lambda and Qdrant. The project involves using LangChain and OpenAI’s embeddings for vector representation, Docker for deployment, and Streamlit for the UI. Detailed instructions and the complete code are provided to help you replicate the setup.

This article shares a personal journey of being diagnosed with endometriosis and the subsequent deep dive into understanding and managing the condition. It highlights the societal neglect of endometriosis despite its prevalence and insufficient funding. The author leverages Large Language Models (LLMs) and data from Reddit to analyze common experiences and advice from fellow sufferers. Using LLMs, the study analyzed Reddit posts to understand sentiments and key topics, revealing frequent emotions like frustration and happiness. It underscores the importance of community support and practical advice for managing endometriosis, advocating for greater societal awareness and research funding.