“llms”
Batch Inference at Scale with Ray
Offline LLM work — scoring, summarization, backfills — needs throughput and reliability, not latency. Distribute thousands of calls with Ray tasks and actors, retries, and structured cost tracking.
Streaming Inference with FastAPI and Server-Sent Events
Turn a blocking LLM call into an incremental, observable stream of tokens with FastAPI StreamingResponse and Server-Sent Events — including cancellation and backpressure.
Fine-Tuning vs. RAG: When to Use Which Technique
Fine-tuning changes how the model behaves; RAG changes what it sees. Use a checklist and a small offline eval to decide between the two — and when to combine them into a hybrid.
Structured Outputs: JSON Schema Validation for LLMs
Ask an LLM for JSON and you will get broken JSON. Learn to constrain generation with Gemini response_schema and Pydantic, then verify the output as an explicit, catchable step.
MLOps for LLMs: Versioning Prompts, Data, and Weights
LLM apps drift for reasons unrelated to weights. Version prompts, datasets, and models against the metrics that decide whether a change is an improvement, using MLflow tracking plus the Model Registry.
RAG Evaluation: Measuring Retrieval Quality Objectively
A RAG pipeline fails in two halves: retrieval and generation. Build a transparent Python eval harness computing hit_rate, MRR, context precision/recall, and faithfulness to know exactly which half to fix.
A Deep Dive into LangChain Expression Language (LCEL)
Learn how to construct complex AI pipelines effortlessly using the declarative power of LangChain Expression Language (LCEL).
Coding with AI using the Agent Development Kit (ADK) and llms.txt
Learn how to enhance your AI coding assistants with the Agent Development Kit documentation using the llms.txt standard across popular tools.