AI Compass: Your Complete Guide to Navigating the AI Landscape as an Engineer

I built a comprehensive open-source learning repository with 100+ guides covering everything from AI fundamentals to production LLM systems. Here’s what’s inside and how to use it.

The Problem: AI Learning is Fragmented

When I started learning AI as a software engineer, I faced a common frustration: resources were scattered everywhere. YouTube tutorials covered basics but skipped production concerns. Academic papers were dense and impractical. Blog posts were either too shallow or assumed PhD-level knowledge.

I wanted a single place that could take an engineer from “What is AI?” all the way to “How do I deploy and monitor an LLM in production?” — with everything in between.

So I built AI Compass.

What is AI Compass?

AI Compass is an open-source learning repository containing 100+ markdown guides organized into a structured curriculum. It’s designed for engineers at every level:

• Complete beginners who don’t know what a neural network is
• Software engineers transitioning into AI/ML roles
• Experienced ML engineers looking to master LLMs and agents
• Engineering managers who need to understand AI capabilities

Everything is MIT licensed, so you can use it however you want — for personal learning, team onboarding, or as a foundation for your own resources.

Repository Structure

Here’s how the content is organized:

ai-compass/
├── ai-fundamentals/       # Start here if you're new to AI
├── foundations/           # Math, ML basics, deep learning
├── learning-paths/        # Structured tracks by experience level
├── prompt-engineering/    # Techniques and patterns
├── llm-systems/           # RAG, agents, tools, multimodal
├── genai-tools/           # GitHub Copilot, Claude, ChatGPT guides
├── agents/                # Agent architectures and patterns
├── practical-skills/      # Building and debugging AI features
├── production-ml-llm/     # MLOps, deployment, monitoring
├── best-practices/        # Evaluation, security, UX
├── ethics-and-responsible-ai/
├── career-and-self-development/
├── resources/             # Curated courses, books, papers
└── projects-and-templates/ # Starter projects with code

What Makes This Different?

1. Learning Paths by Experience Level

Instead of a one-size-fits-all approach, AI Compass provides tailored tracks:

Complete Beginner (No AI Knowledge)

ai-fundamentals/what-is-ai.md
    → ai-fundamentals/key-terminology.md
    → ai-fundamentals/how-models-work.md
    → ai-fundamentals/training-models.md
    → ai-fundamentals/predictive-vs-generative.md

Backend Engineer New to AI (2-4 weeks)

foundations/ml-fundamentals.md
    → foundations/llm-fundamentals.md
    → prompt-engineering/
    → production-ml-llm/deployment-patterns.md
    → llm-systems/rag-and-retrieval.md

Frontend Engineer Transitioning to AI

learning-paths/frontend-engineer-to-ai.md
    → Streaming responses and chat UIs
    → AI UX patterns
    → WebLLM and client-side inference

2. Visual Diagrams with Mermaid

Complex concepts are explained with diagrams, not just walls of text:

3. Production-Ready Content

This isn’t just theory. The repository covers real-world concerns:

• Deployment patterns: Blue-green, canary, shadow deployments for ML
• Cost optimization: Model selection, caching, batching strategies
• Monitoring: What metrics to track, how to detect drift
• Security: Prompt injection prevention, API security, data protection
• Governance: Compliance frameworks (EU AI Act, GDPR, NIST AI RMF)

4. Starter Projects with Code

The projects-and-templates/ directory includes complete starter projects:

Each project includes:

• Complete code examples
• Requirements files
• Architecture diagrams
• Exercises to extend functionality

5. Documentation Templates

Ready-to-use templates for your AI projects:

• Model Card: Document your models for transparency
• Prompt Library: Organize and version your prompts
• Evaluation Report: Structure your LLM evaluations
• Incident Postmortem: Learn from AI system failures

Sample Content Example: Building a Simple RAG System

Here’s a taste of what you’ll find in the repository:

from openai import OpenAI
import numpy as np

client = OpenAI()

class SimpleRAG:
    def __init__(self):
        self.documents = []
        self.embeddings = []

    def add_document(self, text: str):
        embedding = self._embed(text)
        self.documents.append(text)
        self.embeddings.append(embedding)

    def query(self, question: str, top_k: int = 3) -> str:
        # Retrieve relevant documents
        query_embedding = self._embed(question)
        similarities = [
            np.dot(query_embedding, doc_emb)
            for doc_emb in self.embeddings
        ]
        top_indices = np.argsort(similarities)[-top_k:][::-1]
        context = "nn".join([self.documents[i] for i in top_indices])

        # Generate answer
        response = client.chat.completions.create(
            model="gpt-4o",
            messages=[
                {"role": "system", "content": f"Answer based on this context:nn{context}"},
                {"role": "user", "content": question}
            ]
        )
        return response.choices[0].message.content

    def _embed(self, text: str) -> list:
        response = client.embeddings.create(
            model="text-embedding-3-small",
            input=text
        )
        return response.data[0].embedding

Topics Covered

Here’s a non-exhaustive list of what’s in the repository:

AI Fundamentals

• What is AI? History and timeline
• How neural networks work
• Training process explained
• Predictive vs Generative AI

LLM Systems

• Transformer architecture
• RAG and retrieval systems
• Function calling and tools
• Multimodal models
• Open-source model deployment

Prompt Engineering

• Fundamentals and techniques
• Advanced patterns (chain-of-thought, few-shot)
• Real-world examples (customer support, code generation)
• Anti-patterns to avoid

Agents

• Agent architectures (ReAct, Plan-and-Execute)
• Tool design principles
• Multi-agent systems
• Memory and state management

Production ML/LLM

• MLOps fundamentals
• Deployment patterns
• Monitoring and alerting
• Cost optimization
• Governance and compliance

Best Practices

• LLM evaluation strategies
• Security for AI applications
• UX design for AI
• Reproducibility

Ethics and Responsible AI

• Fairness and bias
• Safety and alignment
• Privacy and data protection
• Organizational guidelines

How to Use This Repository

For Self-Paced Learning: Follow the learning paths appropriate to your level. Each section includes explanations, examples, and exercises.

As a Reference: Use GitHub’s search to find specific topics when you need them.

For Team Onboarding: Share relevant learning paths with new team members to standardize AI knowledge.

For Project Planning: Reference the best practices, templates, and checklists when starting new AI features.

Contributing

AI evolves rapidly, and this repository needs to evolve with it. Contributions are welcome:

• Fix errors or outdated information
• Add new examples or exercises
• Improve explanations
• Add coverage of new topics
• Translate content

See the CONTRIBUTING.md for guidelines.

Get Started

Clone the repository and start learning:

git clone https://github.com/satinath-nit/ai-compass.git
cd ai-compass

Or browse directly on GitHub: github.com/satinath-nit/ai-compass

If you find this useful, please give it a star on GitHub! It helps others discover the resource.

Have questions or suggestions? Drop a comment below or open an issue on the repo.

Navigate the AI landscape with confidence. Start your journey today.