About VidyalAI

VidyalAI is a learning resource for engineers who build with large language models. The goal is simple: explain how real systems work, clearly enough to act on and with sources you can check.

The first collection is the LLM System Design series: 25 deep dives on the problems that come up when an LLM feature moves from demo to production. Each article follows the same shape, so you can find what you need quickly:

• TL;DR: the answer in one paragraph.
• Requirements to pin down first: the questions that change the design.
• The design: components, numbers and worked examples.
• How to measure it: metrics, targets and evals.
• Tradeoffs and common mistakes.
• Further reading: the papers and docs behind each claim.

Topics

• Retrieval & RAG: Chunking, hybrid search, reranking and debugging retrieval-augmented generation.
• Cost, Latency & Serving: Make LLM systems cheaper and faster: caching, routing, batching and GPU serving.
• Agents & Tool Use: Reliable tool calling, agent loops, long-term memory and structured output.
• Evaluation & Reliability: Eval suites, LLM-as-a-judge and keeping hallucinations in check.
• Security & Privacy: Defend agents against prompt injection and keep personal data away from models.
• Fine-tuning & Adaptation: When to fine-tune instead of prompting or RAG, and how to do it with LoRA.
• Production Engineering: Streaming, tracing, safe rollbacks and running chat for millions of users.

Corrections

Numbers, prices and model names in this field go stale quickly. Each article cites its sources so you can check them. If you spot something wrong or out of date, please let us know so it can be fixed.