mljourney Why Streaming Matters for LLM Applications A non-streaming LLM response forces users to wait in silence while the model generates its full output — often 3–10 seconds for a typical response. Streaming delivers tokens as they are generated, so the first words appear within 200–500 milliseconds and the response builds progressively on screen. This transforms … Read more
What Is AWS Bedrock? AWS Bedrock is Amazon’s managed AI platform that provides API access to a curated selection of foundation models from multiple providers — Anthropic’s Claude, Meta’s Llama, Mistral AI, Cohere, Amazon’s own Titan models, and others — through a unified AWS API. Rather than integrating with each model provider separately, Bedrock gives … Read more
What Is Text-to-SQL and Why Build It? Text-to-SQL systems translate natural language questions into SQL queries that run against your database, returning answers grounded in your actual data. Instead of requiring users to write SQL or waiting for an analyst to run a report, they ask “What were the top 10 products by revenue last … Read more
What Is pgvector? pgvector is a PostgreSQL extension that adds vector similarity search to the world’s most popular open-source relational database. Instead of running a separate vector database service alongside your Postgres instance, pgvector lets you store embeddings as a native column type and query them with familiar SQL syntax. This dramatically simplifies the infrastructure … Read more
Why LangChain for Agents? LangChain is the most widely-used framework for building LLM-powered agents and applications. Its agent abstractions handle the decision loop — deciding which tools to use, calling them, processing results, and continuing until the task is complete — so you can focus on defining tools and goals rather than orchestration logic. In … Read more
Getting Started with the Anthropic API The Anthropic API provides access to Claude — Anthropic’s family of AI models including Claude Opus, Sonnet, and Haiku. This guide covers everything from initial setup through production-grade usage patterns including streaming, tool use, vision, prompt caching, and async processing. Install the Python SDK and set your API key: … Read more
Why Token Optimisation Matters Every token sent to an LLM API costs money — both input and output tokens. At low volume, the amounts are trivial. At production scale, they become significant operational expenses that compound with every feature addition, every edge case in prompt design, and every traffic spike. An application that sends 5,000 … Read more
What Is Gemini 2.0 Flash? Gemini 2.0 Flash is Google’s second-generation Flash model, released in early 2025. It is designed to be Google’s workhorse model for production applications — fast, cheap, and capable enough to handle the vast majority of real-world tasks without escalating to a frontier model. Flash sits one tier below Gemini 2.0 … Read more
What Is Function Calling? Function calling (also called tool use) allows LLMs to request the execution of functions you define, rather than just returning text. Instead of answering “the weather in London is 18°C” from training knowledge, the model can call a get_weather function with the argument {“location”: “London”}, receive the actual current data, and … Read more
What Is LLM Hallucination? Hallucination refers to LLM outputs that are factually incorrect, fabricated, or unsupported by the model’s input context — presented with the same confident tone as accurate information. The term covers a wide range of failure modes: citing research papers that do not exist, stating incorrect facts with high confidence, generating plausible-sounding … Read more
