🧭 AI Product Field Guide

👩‍💻 Author — Aditi Khare

• Enterprise AI Product, Platform & Applied Research Leader
• Writing on AI research, product thinking, and system architecture

🌐 Presence

🌐 Website: aditikhare.com
🔗 GitHub: AI Product Field Guide
🤗 Hugging Face: AditiShashiKhare
💼 LinkedIn: Aditi Khare

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Notes from the field on building AI products that don’t fall apart in production.

This repository is a practical field guide to designing GenAI, Agentic AI, and Model Context Protocol (MCP)–based products — with a sharp focus on reliability, evaluation, and system-level failure modes.

Not demos.
Not prompt hacks.
Not hype.

⭐ Star this repo if you’re building real AI products beyond the prototype phase.

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Why This Guide Exists

Most AI products don’t fail because the model is bad.

They fail because:

• prompts silently degrade over time
• agents loop, drift, or misuse tools
• RAG systems retrieve data but still answer incorrectly
• context grows until it becomes noise
• evaluation relies on intuition instead of signals

This guide exists to help teams avoid the most common — and most expensive — AI product failures.

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🧭 Products Index (Start Here)

Each product page is part of the AI Product Field Guide — practical notes on building reliable AI systems.

Each product below is a self-contained product note.
All links work both on GitHub and GitHub Pages.

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🧠 GenAI Foundations

1. Prompt Evaluation Engine (When You Have No Labels)
   Estimating prompt reliability when ground truth does not exist.

2. Context Compression Playbook
   Reducing context size while preserving task-critical signal.

3. Why RAG Systems Fail (A Practical Taxonomy)
   Understanding retrieval, context, and answer-synthesis failure modes.

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🤖 Agentic AI

4. Agent Reliability Scorecard (What Breaks First)
   A structured way to evaluate agent behavior, retries, and drift.

5. Tool-Calling Failure Simulator (Conceptual)
   Designing agents that behave predictably when tools partially or silently fail.

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🔌 Model Context Protocol (MCP)

6. MCP Mental Model for Product Teams
   A plain-language explanation of what MCP changes — and when it doesn’t.

7. MCP-First AI Product Architecture
   High-level architectural patterns for MCP-based AI products.

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When This Guide Is Useful

This repository is especially useful if:

• your prompts worked last week but not anymore
• your agent behaves well in demos but not in production
• your RAG system looks correct but answers incorrectly
• your context window keeps growing without improving outcomes
• MCP feels promising but unclear

If none of these resonate, this guide may not be for you.

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What This Repo Is

• A curated collection of public-safe AI product designs
• Focused on failure modes, evaluation, and system boundaries
• Written for builders, product leaders, and applied researchers
• README-first by design — clarity over code volume

Each entry is framed as an AI product, not an experiment.

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What This Repo Is Not

• Not a framework or SDK
• Not end-to-end implementations
• Not proprietary metrics or pipelines
• Not agent hype or demo content

Some details are intentionally excluded.
That’s not an omission — it’s a boundary.

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Design Principles

Most AI failures are system failures, not model failures.

This guide favors:

• evaluation over intuition
• reliability over cleverness
• system boundaries over monoliths
• clarity over completeness

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Status

• 🟢 Actively evolving
• 🟡 Products added deliberately, not frequently
• 🔒 Deeper implementations are intentionally excluded by design.

This repository shares AI product insights only.
Advanced execution details and internal metrics are excluded by design.