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Intermediate 3–5 months 5 Stages

Build Real Applications with Generative AI & LLMs

The generative AI wave has created a massive demand for developers who can build LLM-powered applications. This isn't about training models from scratch — it's about mastering prompt engineering, RAG architectures, fine-tuning, AI agents, and the tools that turn foundation models into production products. This is the fastest-growing and most commercially relevant AI skillset in 2025.

Who This Is For
Developers who want to build applications powered by LLMs and generative AI
Time Commitment
3–5 months
Difficulty
Intermediate
Stages
5 stages, 15 resources

Prerequisites

Working Python proficiency
Basic understanding of APIs and web development
Familiarity with ML concepts (helpful but not required)

The Roadmap

1

Foundation: How LLMs Work

2–3 weeks

Understand the Transformer architecture, how large language models are trained, what tokens are, how context windows work, and the key parameters (temperature, top-p) that control generation. You don't need to train an LLM — but you need to understand what's happening under the hood to build effectively on top of them.

The Transformer architecture — attention, encoder-decoder, self-attention
How LLMs are trained — pre-training, RLHF, instruction tuning
Tokenization — how text becomes numbers and why it matters
Context windows — limits, strategies, and implications for design
Temperature, top-p, and generation parameters
Model landscape — GPT-4, Claude, Llama, Gemini, Mistral, and when to use each
Free Video Andrej Karpathy: Intro to LLMs (1hr Talk) Free Article The Illustrated Transformer Reference Anthropic: Research on LLMs
Large Language Model (LLM) Transformer Attention Mechanism Tokenization Context Window Temperature RLHF Parameters
2

Prompt Engineering & API Mastery

2–3 weeks

Prompt engineering is the primary interface for building with LLMs. Master techniques from basic instruction crafting to advanced strategies like chain-of-thought, few-shot learning, and system prompts. Learn to work with major LLM APIs (OpenAI, Anthropic, Google) and understand rate limits, costs, streaming, and error handling.

Prompt design principles — clarity, specificity, structure
Zero-shot, few-shot, and chain-of-thought prompting
System prompts and role-based instructions
Output formatting — JSON mode, structured outputs, function calling
API integration — OpenAI, Anthropic Claude, Google Gemini APIs
Cost optimization — token counting, caching, model selection
Evaluation — how to measure prompt quality systematically
Free Guide Anthropic Prompt Engineering Guide Free Guide OpenAI Prompt Engineering Guide Free Reference DAIR.AI Prompt Engineering Guide
Prompt Engineering Chain-of-Thought Prompting Zero-Shot & Few-Shot Learning Temperature Hallucination API Inference
3

RAG Systems & Knowledge Integration

3–4 weeks

Retrieval-Augmented Generation (RAG) is the most important design pattern for production LLM applications. Learn to build systems that combine LLMs with external knowledge — documents, databases, APIs — to produce grounded, accurate, up-to-date responses. Master vector databases, embedding models, chunking strategies, and hybrid search.

RAG architecture — retrieval, augmentation, generation pipeline
Embedding models — OpenAI, Cohere, Sentence Transformers, and selection criteria
Vector databases — Pinecone, Weaviate, Chroma, Qdrant, pgvector
Chunking strategies — fixed-size, semantic, recursive, and their trade-offs
Hybrid search — combining vector similarity with keyword (BM25) search
Reranking — improving retrieval quality with cross-encoders
Evaluation — retrieval accuracy, answer faithfulness, and end-to-end metrics
Free Tutorial LangChain RAG Tutorial Free Reference LlamaIndex Documentation Free Tutorials Pinecone Learning Center
Retrieval-Augmented Generation (RAG) Embedding Tokenization Natural Language Processing (NLP) Hallucination Large Language Model (LLM)
4

AI Agents & Tool Use

3–4 weeks

AI agents go beyond simple question-answering — they can reason, plan, use tools, interact with APIs, browse the web, write and execute code, and complete multi-step tasks autonomously. Learn to build agentic systems using frameworks like LangGraph, CrewAI, and the Anthropic tool-use API. Understand the design patterns, guardrails, and failure modes of autonomous AI systems.

What are AI agents — reasoning, planning, and tool use
Function calling / tool use — OpenAI, Anthropic, and Google implementations
Agent frameworks — LangGraph, CrewAI, AutoGen, Semantic Kernel
Multi-agent systems — orchestration, delegation, and collaboration patterns
Memory systems — short-term, long-term, and conversational memory
Guardrails and safety — preventing hallucination, runaway agents, and misuse
Real-world agent applications — coding assistants, research agents, workflow automation
Free Guide Anthropic Tool Use Documentation Free Tutorial LangGraph Documentation Free Course DeepLearning.AI: Building Agentic RAG
AI Agent Large Language Model (LLM) Chain-of-Thought Prompting Retrieval-Augmented Generation (RAG) Hallucination AI Safety
5

Fine-Tuning & Production Deployment

3–4 weeks

When prompting and RAG aren't enough, fine-tuning customizes a model's behavior for your specific use case. Learn when fine-tuning is (and isn't) the right solution, how to prepare training data, and techniques like LoRA and QLoRA for efficient fine-tuning. Then deploy your AI applications with proper monitoring, cost management, and safety guardrails.

When to fine-tune vs. prompt vs. RAG — the decision framework
Training data preparation — formatting, quality, and quantity
Parameter-Efficient Fine-Tuning — LoRA, QLoRA, adapters
Fine-tuning with Hugging Face, OpenAI, and Anthropic APIs
Deployment patterns — serverless, containerized, edge deployment
Cost optimization — model routing, caching, batching
Monitoring and evaluation in production — drift, quality, latency, cost
Free Reference Hugging Face PEFT Documentation Tool Modal: Serverless GPU for Inference Free SDK Vercel AI SDK
Fine-tuning Model Compression Parameters GPU & TPU Edge AI API Benchmark Responsible AI

Tools & Technologies

LangChain / LlamaIndex
OpenAI / Anthropic APIs
Vector Databases
Hugging Face
Vercel / Next.js
Docker

Career Outcomes

AI Application Developer ($140K–$250K+)
LLM/GenAI Engineer
AI Solutions Architect
AI Startup Founder or Technical Co-founder

Frequently Asked Questions

Do I need to know machine learning to build with LLMs?

Not necessarily. Many LLM application developers use pre-trained models through APIs without deep ML knowledge. Understanding basic ML concepts helps with fine-tuning and evaluation, but prompt engineering, RAG, and agent development are accessible to any developer with Python experience.

What is the difference between prompt engineering and fine-tuning?

Prompt engineering customizes model behavior through instructions given at inference time — it's fast, flexible, and requires no training data. Fine-tuning modifies the model's weights using custom training data — it produces more consistent behavior but requires training data, compute, and expertise. Most applications should start with prompting + RAG and only fine-tune when needed.

How much does it cost to build LLM applications?

API costs vary widely: GPT-4o costs ~$2.50-10 per million tokens; Claude Sonnet is similar; open-source models on your own infrastructure can be cheaper at scale. A typical startup spends $500-5,000/month on LLM APIs during development. The key is optimizing with caching, model routing (using cheaper models when possible), and efficient prompt design.

Should I use open-source or proprietary LLMs?

Both have roles. Proprietary models (GPT-4, Claude) offer the best quality with zero infrastructure overhead — ideal for most applications. Open-source models (Llama, Mistral) give you full control, privacy, and lower marginal costs at scale — essential for sensitive data or high-volume use cases. Many production systems use both: expensive models for complex tasks, cheaper models for simple ones.