Inferix Learn
LLM Foundations
Build production-grade LLM applications with retrieval, evaluation, and observability.
Best for: Application engineers and platform teams building chat, search, and copilots.
What you will cover
- Prompt and context architecture patterns
- RAG pipelines with quality guardrails
- Latency, cost, and reliability optimization
Progress and resume
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Chapter 1: Prompt systems and context windows
Chapter notes
Use structured prompts and explicit context budgets.
const systemPrompt = "You are a concise assistant";
Interactive exercise
Adjust context size and observe estimated per-request token budget.
Context chunks: 30 | Estimated tokens: 7680
Quiz
Which pattern most directly reduces prompt drift in long sessions?
Chapter 2: Retrieval and knowledge grounding
Chapter notes
Ground answers with retrieved passages and explicit citations.
results = retriever.search(query, k=5)
Interactive exercise
Adjust context size and observe estimated per-request token budget.
Context chunks: 30 | Estimated tokens: 7680
Quiz
What is the main purpose of retrieval augmentation?