🧠 The AI Developer’s Cheat Sheet — From LLMs to Agents

1. 🧩 LLMs (Large Language Models)

What they are:
Neural networks trained on massive text datasets to predict the next token (e.g., GPT, Claude, Llama).

Developer view:

• Built on the Transformer architecture.
• Excel at reasoning, summarization, and generation.
• Not reliable for up-to-date factual recall.

Use for: reasoning, summarization, code generation, chat systems.

2. 🔢 Embeddings

What they are:
Numeric vector representations of text that capture meaning.
Similar texts → similar vectors.

Why they matter:
They let you compare meaning instead of just matching keywords.

Use cases:

• Semantic search
• Clustering & recommendations
• Retrieval-Augmented Generation (RAG)

Example:

vector = client.embeddings.create(
    model="text-embedding-3-small",
    input="LangChain connects LLMs with data."
)

3. 🗂️ Vector Databases

What they are:
Databases optimized for high-dimensional vector search using ANN algorithms (HNSW, IVF+PQ, etc.).

Popular tools: Pinecone, Qdrant, Weaviate, Milvus, FAISS.

Mental model:

Elasticsearch — but for meaning.

Use for:
Fast semantic search, document retrieval, and AI-powered recommendations.

4. ⚙️ RAG (Retrieval-Augmented Generation)

Purpose:
Let the LLM “look things up” before it answers.

Pipeline:

1. User query → embedding
2. Search vector database for similar chunks
3. Retrieve top-k matches
4. Insert them into the LLM prompt
5. Generate grounded answer

Example flow:

Query → Embed → Retrieve → Build Prompt → LLM → Answer

Use for:
Knowledge-based chatbots, internal assistants, contextual Q&A.

5. 🧠 Fine-Tuning

Definition:
Continue training a base model on your custom dataset to teach style, structure, or task patterns.

Use for:

• Domain-specific tone (medical, legal, etc.)
• Consistent output format (JSON, code, QA)
• Company-specific writing or voice

Not for: updating factual knowledge (RAG is better).

Tip:
Use LoRA or QLoRA for efficient fine-tuning.

6. 💬 Prompt Engineering

Definition:
Crafting prompts that guide model behavior and ensure consistent outputs.

Common techniques:

• Few-shot prompting: show examples
• Chain-of-Thought: “Let’s think step by step.”
• ReAct: “Reason + Act” (for agentic reasoning)
• Structured prompts: enforce formats like JSON or markdown

Pro tip:

Prompting = programming with language.

7. ⚡ Advanced Prompt Strategies

1. Prompt Chaining
Break complex tasks into smaller LLM calls for modularity and control.

2. Self-Consistency
Run multiple generations → vote or average best results.

3. Reflexion
Ask the model to critique and improve its own output.

4. Tree of Thoughts (ToT)
Branch reasoning paths, evaluate, then merge or select the best.

5. Guardrails / Validation
Enforce schema or format compliance (Guardrails, Instructor libraries).

6. Dynamic Prompting
Build prompts with variables and code templates at runtime.

7. Auto-Prompting
Automatically optimize prompts through evaluation loops.

8. 🤖 AI Agents

Definition:
LLMs that can reason and act — calling APIs, running tools, or performing steps autonomously.

How they work:

1. LLM reasons: “I need data.”
2. Calls an external tool (API, function).
3. Uses result to continue reasoning.

Frameworks: LangChain, AutoGen, CrewAI, OpenAI Responses API (with MCP).

Mental model:

LLM = brain.
Agent = brain + hands.

9. 🧩 MCP (Model Context Protocol)

Origin:
Created by Anthropic, later adopted by OpenAI and others.

Purpose:
A standard protocol that lets models access tools, files, and data sources consistently and securely.

Analogy:

MCP is the USB standard for AI tools.

10. 🧰 Putting It All Together

🧭 TL;DR

• RAG → teaches the AI what to talk about.
• Fine-Tuning → teaches it how to talk.
• Prompting → controls how it behaves live.
• Agents → make it do things.
• MCP → connects everything together.

💡 Final Thought

AI engineering isn’t about replacing logic with magic —
it’s about orchestrating reasoning, data, and tools.