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3 changes: 2 additions & 1 deletion docs.json
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"pages": [
"index",
"quickstart",
"development"
"development",
"guides/ai-llms"
]
},
{
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55 changes: 55 additions & 0 deletions guides/ai-llms.mdx
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---
title: "AI and LLMs 101"
description: "A practical introduction to Artificial Intelligence and Large Language Models"
---

## What is AI?

Artificial Intelligence (AI) refers to systems that perform language understanding, pattern recognition, decision-making, and content generation.

<Info>
This playbook focuses on language AI that powers product experiences.
</Info>

## What are LLMs?

Large Language Models (LLMs) predict the next token to generate text, code, and summaries from prompts and optional context.

## How LLMs work (at a glance)

- **Tokenization:** Text is split into subword tokens.
- **Transformers:** Self-attention weighs relevant context across long inputs.
- **Training:** Pretraining plus alignment (instruction tuning, RLHF/DPO) steers behavior.

<Tip>
Treat outputs as plausible text, not facts—verify critical claims.
</Tip>

## Strengths and limits

- **Strengths:** drafting, summarization, Q&A, translation, flexible interfaces
- **Limits:** hallucinations, outdated knowledge, prompt sensitivity, weak math/logic without scaffolding

<Warning>
Use human review and guardrails for high‑risk flows.
</Warning>

## Common terms

- **Prompt:** Instructions and context for the model
- **Context window:** Max tokens of input plus output
- **Temperature/top‑p:** Controls randomness/diversity
- **Tools/functions:** External calls (search, code, databases)

## Choosing a model

Match the model to the job:

- **General purpose:** Balanced instruction-following and safety
- **Reasoning-focused:** Trained for multi-step reasoning
- **Cost/latency constrained:** Smaller/open or on‑prem/edge deployments

<Note>
Evaluate on your scenarios and rubrics; track agreement with human reviews.
</Note>