Elysiate blog
Practical guides for privacy-first developer tools, SEO and content operations, data and file workflows, cloud and API security, AI engineering, and sustainable freelance work. Everything here supports the same philosophy as our browser-based utilities: your data stays on your device until you choose otherwise.
For tabular data, the CSV tools hub covers validation and conversion in the browser, and the CSV topic index lists every CSV-tagged guide in one place.
Page 74 of 126
- LLM Application Architecture Explained
A practical guide to LLM application architecture showing how prompts, models, retrieval, tools, structured outputs, guardrails, evals, and operations fit together in real AI systems.
- Hybrid Search vs Vector Search
A practical guide to hybrid search vs vector search, including exact-match retrieval, semantic retrieval, reranking, and production tradeoffs for real-world knowledge systems.
- How To Write Better Prompts For LLM Apps
A practical guide to writing better prompts for LLM apps, with production patterns for clarity, grounding, structured outputs, examples, reusable prompt design, and safer iteration.
- How To Test AI Agents Systematically
A practical guide to testing AI agents systematically, with clear methods for scenario design, trace-based grading, tool-use evaluation, regression testing, and production monitoring.
- How To Reduce Tool Overload In Agentic Systems
A practical guide to reducing tool overload in agentic systems, with clear patterns for smaller tool surfaces, better routing, specialist workflows, and more reliable production agents.
- How To Move From AI Prototype To Production
A practical guide to moving from AI prototype to production with clear steps for hardening architecture, adding evals, improving reliability, controlling cost, and launching safely.
- How To Improve RAG Retrieval Quality
A practical guide to improving RAG retrieval quality, with concrete fixes for chunking, indexing, metadata, hybrid retrieval, reranking, and retrieval evaluation in production.
- How To Evaluate An LLM App Properly
A practical guide to evaluating an LLM app properly with clear methods for dataset design, automated and human grading, trace-based debugging, regression testing, and production monitoring.
- How To Design A Production Ready LLM System
A practical guide to designing a production ready LLM system from scoping and architecture to evals, guardrails, tracing, latency, cost control, and safe rollout.
- How To Debug Tool Calling Failures In LLM Apps
A practical guide to debugging tool calling failures in LLM apps, from wrong tool selection and malformed arguments to silent execution errors, policy failures, and agent trace analysis.