Mudit Bachhawat

Tag: llm

  • Scaling AI Teams and Workload

    Reading Time: 4 minutes

    Scaling AI teams and workloads involves organizing efforts across various levels as companies grow. Initially, teams focus on complete products like search, recommendations, or chatbots, with potential sub-product divisions. As systems mature, scaling can occur at the component level (e.g., retrieval, ranking), feature level (e.g., knowledge graphs, trust signals), or by targeting specific user cohorts…

  • LLM-as-a-Judge for AI Systems

    Reading Time: 10 minutes

    Introduction Common Patterns of LLM-as-a-Judge Method Basic Evaluating Judge Model Improving Judge Performance Scaling Judgments Closing References

  • Keeping Up with RAGs: Recent Developments and Optimization Techniques

    Reading Time: 10 minutes

    [medium discussion] RAG Basics Indexing Indexing Inference Inference Query Query Vector DB Vector DB Response Response nn scan nn scan Embedding Embedding Prompt +Passages Prompt +… LLM LLM Retrieval Retrieval Generation Generation Documents Documents Chunking Chunking Chunks Chunks LLM LLM Embeddings Embeddings write writeText is not SVG – cannot display Chunking Embedding Model Fine-tuning Embedding…

  • External Knowledge in LLMs

    External Knowledge in LLMs

    Reading Time: 10 minutes

    [medium and substack discussion] LLMs are trained on finite set of data. While it can answer wide variety of questions across multiple domain, it often fails to answer questions which are highly domain-specific and out of its training context. Additionally, training LLMs from scratch for any new information is not possible like traditional models with…