Building the Machine:
Enterprise AI Stack
A chat interface is not an enterprise architecture. To run AI in production, you need a robust stack including Vector Databases, Orchestration Layers, and API Gateways. This module covers the engineering blueprints.
The Enterprise AI Stack
Just like the LAMP stack powered the early web, the "AI Stack" is the new standard. It moves from raw GPU compute at the bottom to Agentic Applications at the top.
The Enterprise GenAI Stack
From raw compute to business applications.
Application Layer
Model Gateway (API)
ML Platform
Infrastructure Layer
Retrieval-Augmented Generation (RAG)
RAG is the industry standard for connecting LLMs to your private data. Instead of retraining the model (which is expensive), we "retrieve" the relevant facts from your database and "augment" the prompt before sending it to the AI.
Retrieval-Augmented Generation (RAG)
Bridging the gap between LLM reasoning and your proprietary data.
1. User Query
The user asks a question (e.g., 'What is our refund policy?').
Why RAG?
- Accuracy: Reduces hallucinations by grounding the model in facts.
- Freshness: No need to re-train the model when data changes; just update the database.
- Security: Strict access control on retrieved documents (ACLs).
Business Applications
Fine-Tuning vs. Prompting
Should you just write a better prompt, or do you need to train the model? This spectrum helps you decide based on cost, effort, and the level of control required.
The Cost of Customization
Choosing the right approach for your business needs.
Prompt Eng
In-Context Learning
The starting point. Use zero-shot or few-shot prompting. Zero infra cost, but limited by context window and base model knowledge.
The Foundation Model Lifecycle
From raw compute to polished product.
Learning the Language
Next-Token Prediction. The model learns grammar, facts, and reasoning patterns.
The Art of Prompting
Prompting is programming in natural language. Structure matters. Learn the anatomy of a robust enterprise prompt and advanced techniques like "Chain-of-Thought".
Anatomy of a Perfect Prompt
Constructing prompts is engineering, not guessing. See how structure affects quality.
Technique Workbench
From basic instructions to advanced reasoning chains.
Asking the model to perform a task without any examples.
Fastest, but relies entirely on the model's pre-training. Can be vague.
Autonomous Agents
The frontier of AI. Agents move beyond "Chat" to "Action". They can plan multi-step workflows, use tools (like Calculators or APIs), and verify their own work.
Agents: Multi-Step Reasoning
Moving from "Chat" to "Action". Agents can use tools to solve complex problems.
Observe
Read user input & environment state.
The Final Layer: Trust
A powerful system is useless if it's not secure and compliant. Let's explore Governance.
Go to Module 5: Security & Governance