inceptions.xyz

A practical comparison of semantic, keyword, and hybrid search, with clear guidance on when each approach fits best.

A practical guide to prompt engineering for extracting structured data from messy text, with templates, maintenance routines, and update signals.

A practical guide to building and maintaining a text summarizer app with an LLM API, from prompt design to chunking and evaluation.

A practical LLM observability guide for tracking logs, traces, and feedback loops in production AI apps.

A practical comparison of AI agent frameworks, with guidance on orchestration, memory, observability, deployment, and best-fit scenarios.

A practical hub for reducing prompt injection risk in RAG apps and AI agents with layered defenses, testing methods, and update triggers.

A reusable prompt testing workflow for teams that need versioning, scoring, QA reviews, and steady prompt improvement over time.

A practical workflow for building an AI support bot that answers from your knowledge base, handles handoffs, and stays maintainable over time.

A reusable checklist for evaluating AI agents on task success, tool use, safety, and production reliability.

A practical few-shot prompt guide with examples, maintenance tips, and update signals for more consistent LLM outputs.

A practical buyer’s guide to comparing vector databases for RAG by filtering, indexing, pricing shape, and developer experience.

A practical framework for benchmarking LLMs by use case, with repeatable ways to compare speed, quality, reliability, and cost.

A practical beginner guide to building, testing, and maintaining a RAG chatbot that stays useful as your content and stack evolve.

A reusable production checklist to reduce LLM hallucinations across chatbots, RAG apps, content workflows, and AI agents.

A practical comparison of JSON mode, function calling, and structured outputs for reliable LLM apps, tools, and automation workflows.

A practical framework to estimate, test, and reduce AI API costs without lowering output quality.

A practical framework for comparing open source LLMs for local and private use by quality, hardware, licenses, and real-world fit.