Roadmap - GenAI & Agentic AI Engineering
Table of Contents
Overview
This roadmap is designed for ML/AI Engineers who need to build production-ready GenAI and Agentic AI systems. Focus is on practical implementation, deployment, and maintaining systems at scale.
Target: Master GenAI + Agentic AI + LLM engineering skills Goal: Production proficiency Outcome: Deploy and maintain robust GenAI and autonomous agent systems
Phases
Phase 0: Prerequisites
Python & Data Fundamentals
| Topic | Details | Deliverable |
|---|---|---|
| Python essentials | Data types, functions, classes, async/await | 10 Python scripts with async |
| NumPy for arrays | Array operations, indexing, broadcasting | Numerical computation tasks |
| Pandas basics | DataFrames, filtering, grouping, time series | 3 data manipulation notebooks |
| Data cleaning | Missing values, duplicates, text preprocessing | Clean 3 messy text datasets |
| Visualization | Matplotlib, Seaborn, Plotly for plotting | 10 different plot types |
| JSON & APIs | Working with JSON, REST API consumption | API client implementation |
Tools Setup:
- Python 3.10+, Jupyter Lab
- Virtual environments (venv/conda)
- Git for version control
- OpenAI/Anthropic API keys
ML & NLP Basics
| Topic | Details | Deliverable |
|---|---|---|
| ML fundamentals | Supervised, unsupervised, reinforcement learning | ML concepts notebook |
| NLP basics | Tokenization, embeddings, text processing | Text processing pipeline |
| Transformers intro | Attention mechanism, BERT, GPT architecture | Transformer explanation doc |
| Vector embeddings | Semantic similarity, embedding spaces | Embedding similarity tool |
| Model evaluation | Metrics for generation tasks (BLEU, ROUGE, perplexity) | Evaluation framework |
| Prompt engineering basics | Zero-shot, few-shot, chain-of-thought | 20 prompt examples |
Key Concepts:
- Understand transformer architecture
- Grasp attention mechanisms
- Know embedding representations
- Basic prompt design
Phase 1: GenAI Foundations
Large Language Models (LLMs)
| Topic | Details | Deliverable |
|---|---|---|
| LLM fundamentals | GPT, Claude, LLaMA architecture; Practice: Compare LLM responses | LLM comparison document |
| Model capabilities | Text generation, summarization, Q&A, translation; Practice: Test capabilities | Capability assessment |
| Model limitations | Hallucinations, bias, context limits; Practice: Identify failure modes | Limitation analysis doc |
| Token economics | Token counting, cost optimization; Practice: Calculate costs | Cost calculator tool |
| Model selection | Choosing right model for task; Practice: Decision framework | Model selection guide |
| API basics | OpenAI, Anthropic, Hugging Face APIs; Practice: Build API clients | Working API integrations |
Key Skills:
- Use multiple LLM APIs
- Understand token limits
- Recognize hallucinations
- Optimize for cost
Prompt Engineering
| Topic | Details | Deliverable |
|---|---|---|
| Prompt patterns | Instructions, examples, templates; Practice: Create 50 prompts | Prompt library |
| Few-shot learning | Example selection, ordering; Practice: Few-shot experiments | Few-shot prompt collection |
| Chain-of-thought | Step-by-step reasoning prompts; Practice: CoT for complex tasks | CoT prompt templates |
| Role prompting | System messages, persona design; Practice: Role-based prompts | Role prompt library |
| Output formatting | JSON, structured outputs; Practice: Structured generation | Formatted output examples |
| Prompt optimization | Iterative refinement, A/B testing; Practice: Optimize 10 prompts | Optimization framework |
Prompt Engineering Tools:
- LangSmith for tracking
- Prompt versioning system
- Evaluation harness
Retrieval-Augmented Generation (RAG)
| Topic | Details | Deliverable |
|---|---|---|
| RAG fundamentals | Retrieval + generation pipeline; Practice: Basic RAG system | Simple RAG app |
| Vector databases | Pinecone, Milvus, Weaviate, pgvector; Practice: Compare vector DBs | Vector DB comparison |
| Embeddings | Sentence transformers, OpenAI embeddings; Practice: Embedding pipeline | Embedding service |
| Chunking strategies | Text splitting, semantic chunking; Practice: Optimal chunking | Chunking framework |
| Retrieval methods | Dense, sparse, hybrid retrieval; Practice: Compare retrieval | Retrieval comparison |
| RAG evaluation | Retrieval accuracy, answer quality; Practice: RAG metrics | RAG evaluation suite |
RAG Architecture:
- Document ingestion pipeline
- Vector storage
- Retrieval layer
- Generation layer
LLM Application Frameworks
| Topic | Details | Deliverable |
|---|---|---|
| LangChain basics | Chains, agents, tools; Practice: Build 5 chains | LangChain examples |
| LlamaIndex basics | Data connectors, query engines; Practice: Build index | LlamaIndex RAG app |
| Framework comparison | LangChain vs LlamaIndex vs custom; Practice: Same app in each | Comparison analysis |
| Memory management | Conversation history, context windows; Practice: Memory system | Memory implementation |
| Tool integration | APIs, databases, external tools; Practice: Tool calling | Tool integration examples |
| Error handling | Retries, fallbacks, timeouts; Practice: Robust app | Error handling patterns |
Key Frameworks:
- LangChain for workflows
- LlamaIndex for RAG
- Spring AI for Java
- LangChain4j for Java
Fine-tuning & Model Customization
| Topic | Details | Deliverable |
|---|---|---|
| When to fine-tune | Cost-benefit analysis; Practice: Decision framework | Fine-tuning guide |
| Dataset preparation | Data collection, formatting, cleaning; Practice: Prepare dataset | Training dataset |
| LoRA/QLoRA | Parameter-efficient fine-tuning; Practice: LoRA fine-tune | Fine-tuned model |
| OpenAI fine-tuning | GPT fine-tuning API; Practice: Fine-tune GPT | Custom GPT model |
| Evaluation | Model comparison, metrics; Practice: Eval framework | Evaluation pipeline |
| Deployment | Serving fine-tuned models; Practice: Deploy model | Model serving endpoint |
Fine-tuning Platforms:
- OpenAI fine-tuning API
- Hugging Face Trainer
- Together.ai
- Modal for training
Phase 1 Checkpoint: Can you build RAG systems? Can you optimize prompts? Can you integrate LLM APIs effectively?
Phase 2: Agentic AI Fundamentals
Understanding AI Agents
| Topic | Details | Deliverable |
|---|---|---|
| Agent definition | Autonomy, reactivity, goal-oriented; Practice: Analyze agent types | Agent taxonomy doc |
| ReAct pattern | Reasoning + Acting loop; Practice: Implement ReAct | ReAct agent |
| Agent components | Perception, reasoning, action, memory; Practice: Build basic agent | Simple agent system |
| Agent types | Reactive, deliberative, hybrid; Practice: Compare types | Agent type comparison |
| Tool use | Function calling, tool selection; Practice: Multi-tool agent | Tool-using agent |
| Agent evaluation | Success rate, efficiency, cost; Practice: Eval metrics | Agent evaluation framework |
Agent Patterns:
- ReAct (Reason + Act)
- Plan-and-Execute
- Reflection
- Self-refinement
Single Agent Systems
| Topic | Details | Deliverable |
|---|---|---|
| LangChain agents | Agent types, tools, memory; Practice: Build 3 agents | LangChain agent examples |
| Tool creation | Custom tools, API wrappers; Practice: Create 5 tools | Custom tool library |
| Planning strategies | Task decomposition, planning; Practice: Planning agent | Planning system |
| Memory systems | Short-term, long-term memory; Practice: Memory implementation | Memory-enabled agent |
| Error recovery | Retries, fallbacks, graceful degradation; Practice: Robust agent | Error handling agent |
| Agent optimization | Reduce iterations, cost; Practice: Optimize agent | Optimized agent |
Key Technologies:
- LangChain Agents
- OpenAI Function Calling
- Anthropic Tool Use
- AutoGPT patterns
Multi-Agent Systems
| Topic | Details | Deliverable |
|---|---|---|
| Multi-agent patterns | Collaboration, delegation, hierarchy; Practice: Multi-agent system | Multi-agent app |
| LangGraph basics | Graph-based workflows; Practice: Build graphs | LangGraph workflows |
| Agent coordination | Communication, task distribution; Practice: Coordinated agents | Coordination system |
| CrewAI basics | Role-based teams; Practice: Build crew | CrewAI team |
| AutoGen2 basics | Agent collaboration; Practice: AutoGen system | AutoGen workflow |
| Comparison | When to use which framework; Practice: Framework selection | Framework guide |
Multi-Agent Frameworks:
- LangGraph for orchestration
- CrewAI for teams
- AutoGen2 for collaboration
Agent Tools & Integration
| Topic | Details | Deliverable |
|---|---|---|
| Web search | Integrate search APIs; Practice: Search tool | Search agent |
| Code execution | Sandboxed code running; Practice: Code interpreter | Code execution agent |
| Database access | SQL generation, queries; Practice: Database agent | DB query agent |
| API integration | REST, GraphQL APIs; Practice: API agent | API integration agent |
| File operations | Read, write, process files; Practice: File agent | File processing agent |
| Tool composition | Chain multiple tools; Practice: Complex workflows | Multi-tool workflows |
Tool Categories:
- Information retrieval (web, docs)
- Data processing (files, DBs)
- Code execution (Python, shell)
- External APIs (weather, news, etc.)
Agentic Workflows
| Topic | Details | Deliverable |
|---|---|---|
| Task planning | Break down complex tasks; Practice: Planning system | Task planner |
| Execution loops | Plan → Execute → Reflect; Practice: Execution loop | Agentic loop |
| Self-reflection | Agent evaluates own output; Practice: Reflection agent | Self-reflection system |
| Iterative refinement | Improve through iterations; Practice: Refinement loop | Iterative agent |
| Human-in-the-loop | When to ask for help; Practice: HITL system | Human feedback agent |
| Workflow orchestration | Complex multi-step workflows; Practice: Orchestrated workflow | Production workflow |
Workflow Patterns:
- Sequential execution
- Parallel execution
- Conditional branching
- Loop until success
Phase 2 Checkpoint: Can you build autonomous agents? Can you orchestrate multi-agent systems? Can you design agentic workflows?
Phase 3: Production Engineering
LLM Application Architecture
| Topic | Details | Deliverable |
|---|---|---|
| System design | Architecture patterns for LLM apps; Practice: Design document | Architecture diagram |
| API design | RESTful APIs for LLM services; Practice: FastAPI service | Production API |
| Caching strategies | Response caching, embedding cache; Practice: Caching layer | Caching system |
| Rate limiting | API quotas, backpressure; Practice: Rate limiter | Rate limiting system |
| Load balancing | Distribute requests; Practice: Load balancer | Load balancing setup |
| Scalability | Horizontal scaling patterns; Practice: Scalable architecture | Scaling plan |
Architecture Patterns:
- Synchronous APIs
- Asynchronous processing
- Event-driven architecture
- Microservices
Prompt Management & Versioning
| Topic | Details | Deliverable |
|---|---|---|
| Prompt versioning | Track prompt changes; Practice: Version control | Prompt versioning system |
| Prompt templates | Reusable templates; Practice: Template library | Template system |
| Prompt testing | Automated prompt evaluation; Practice: Test suite | Prompt test framework |
| A/B testing | Compare prompt versions; Practice: A/B framework | A/B testing system |
| Prompt optimization | Data-driven improvement; Practice: Optimization pipeline | Optimization framework |
| Prompt registry | Centralized prompt storage; Practice: Prompt registry | Prompt management system |
Tools:
- Langfuse for prompt management
- LangSmith for versioning
- Custom prompt registry
Observability & Monitoring
| Topic | Details | Deliverable |
|---|---|---|
| LLM observability | Trace LLM calls, latency, tokens; Practice: Observability setup | Monitoring dashboard |
| LangSmith integration | Track chains and agents; Practice: LangSmith setup | LangSmith monitoring |
| Langfuse integration | Prompt tracking, analytics; Practice: Langfuse setup | Langfuse dashboard |
| Cost monitoring | Track API costs per request; Practice: Cost tracking | Cost dashboard |
| Quality monitoring | Response quality metrics; Practice: Quality monitoring | Quality metrics |
| Alerting | Anomaly detection, alerts; Practice: Alert system | Alert notifications |
Monitoring Tools:
- LangSmith for LangChain
- Langfuse for multi-framework
- Prometheus for metrics
- Grafana for visualization
Safety & Content Moderation
| Topic | Details | Deliverable |
|---|---|---|
| Content filtering | Input/output moderation; Practice: Moderation system | Content filter |
| Prompt injection defense | Detect and prevent attacks; Practice: Defense mechanisms | Injection defense |
| PII detection | Identify sensitive data; Practice: PII detection | PII filter |
| Bias mitigation | Reduce model bias; Practice: Bias testing | Bias mitigation strategy |
| Safety guardrails | Prevent harmful outputs; Practice: Guardrails | Safety system |
| Compliance | GDPR, data privacy; Practice: Compliance checklist | Compliance framework |
Safety Tools:
- OpenAI Moderation API
- Guardrails AI
- NeMo Guardrails
- Custom filters
Testing & Evaluation
| Topic | Details | Deliverable |
|---|---|---|
| Unit testing | Test individual components; Practice: Test suite | Unit tests |
| Integration testing | Test end-to-end flows; Practice: Integration tests | Integration test suite |
| Evaluation datasets | Create test datasets; Practice: Eval dataset | Test dataset |
| Automated evaluation | BLEU, ROUGE, semantic similarity; Practice: Eval pipeline | Automated eval system |
| Human evaluation | Manual quality assessment; Practice: Eval framework | Human eval process |
| CI/CD for LLMs | Continuous testing; Practice: CI/CD pipeline | CI/CD setup |
Testing Frameworks:
- pytest for Python
- LangChain evaluation
- Custom eval harnesses
Deployment & MLOps
| Topic | Details | Deliverable |
|---|---|---|
| Containerization | Docker for LLM apps; Practice: Dockerfile | Dockerized app |
| Orchestration | Kubernetes basics; Practice: K8s deployment | K8s manifests |
| Model serving | vLLM, TensorRT-LLM; Practice: Model server | Model serving endpoint |
| Serverless deployment | AWS Lambda, Modal; Practice: Serverless app | Serverless deployment |
| Model versioning | Track model versions; Practice: Versioning system | Version control |
| Rollback strategies | Safe deployment, quick rollback; Practice: Rollback mechanism | Rollback system |
Deployment Platforms:
- Docker + Kubernetes
- AWS (Lambda, ECS, SageMaker)
- Modal for inference
- Vercel/Netlify for frontends
Phase 3 Checkpoint: Can you deploy production LLM apps? Can you monitor and maintain systems? Can you ensure safety and quality?
Phase 4: Advanced Topics
Advanced RAG Techniques
| Topic | Details | Deliverable |
|---|---|---|
| Hybrid retrieval | Dense + sparse + keyword; Practice: Hybrid system | Hybrid retrieval |
| Query rewriting | Expand/refine queries; Practice: Query rewriter | Query optimization |
| Re-ranking | Reorder retrieval results; Practice: Re-ranker | Re-ranking system |
| Contextual compression | Compress retrieved context; Practice: Compression | Compression system |
| Multi-query RAG | Generate multiple queries; Practice: Multi-query system | Multi-query RAG |
| Agentic RAG | Agents decide retrieval strategy; Practice: Agentic RAG | Agentic retrieval |
Advanced RAG:
- Self-RAG (reflection)
- Corrective RAG
- Adaptive retrieval
- Graph RAG
Advanced Agent Patterns
| Topic | Details | Deliverable |
|---|---|---|
| Plan-and-Execute | Planning then execution; Practice: Plan-Execute agent | Plan-Execute system |
| Reflection agents | Self-evaluation, refinement; Practice: Reflection system | Reflective agent |
| Meta-agents | Agents managing agents; Practice: Meta-agent system | Meta-agent orchestrator |
| Learning agents | Agents that improve; Practice: Learning agent | Adaptive agent |
| Autonomous agents | Fully autonomous systems; Practice: Autonomous agent | Autonomous system |
| Agent benchmarks | Test agent capabilities; Practice: Benchmark suite | Agent benchmark |
Agent Architectures:
- BabyAGI pattern
- AutoGPT pattern
- GPT-Engineer pattern
- MetaGPT pattern
Long-Context & Memory
| Topic | Details | Deliverable |
|---|---|---|
| Long-context models | Use 100K+ context windows; Practice: Long-context app | Long-context system |
| Context compression | Summarize long contexts; Practice: Compression | Context compressor |
| External memory | Vector memory, knowledge graphs; Practice: External memory | Memory system |
| Conversation memory | Maintain conversation state; Practice: Conversation memory | Conversation system |
| Episodic memory | Remember past interactions; Practice: Episodic memory | Episodic memory |
| Memory retrieval | Efficient memory access; Practice: Memory retrieval | Retrieval system |
Memory Systems:
- Vector stores for semantic memory
- Graph databases for episodic
- Redis for short-term cache
Multi-Modal AI
| Topic | Details | Deliverable |
|---|---|---|
| Vision-Language models | GPT-4V, Claude Vision; Practice: Vision app | Vision-language app |
| Image generation | DALL-E, Stable Diffusion; Practice: Image gen app | Image generation system |
| Audio processing | Whisper, TTS; Practice: Audio app | Audio processing app |
| Video understanding | Video analysis with LLMs; Practice: Video analyzer | Video analysis system |
| Multi-modal agents | Agents using multiple modalities; Practice: Multi-modal agent | Multi-modal system |
| Document understanding | OCR + LLM for documents; Practice: Doc processor | Document processor |
Multi-Modal Models:
- GPT-4V, Claude 3
- DALL-E 3, Midjourney
- Whisper, ElevenLabs
- Runway, Pika
Production Optimization
| Topic | Details | Deliverable |
|---|---|---|
| Latency optimization | Reduce response time; Practice: Latency profiling | Optimized system |
| Cost optimization | Reduce API costs; Practice: Cost analysis | Cost reduction plan |
| Caching strategies | Semantic caching, exact match; Practice: Advanced caching | Caching system |
| Batch processing | Efficient batch inference; Practice: Batch processor | Batch system |
| Model distillation | Smaller, faster models; Practice: Distillation | Distilled model |
| Inference optimization | vLLM, TensorRT-LLM; Practice: Optimized inference | Fast inference system |
Optimization Techniques:
- Prompt caching
- Semantic caching
- Response streaming
- Quantization
Enterprise & Security
| Topic | Details | Deliverable |
|---|---|---|
| Enterprise architecture | Multi-tenant systems; Practice: Enterprise design | Enterprise architecture |
| Authentication & authorization | OAuth, RBAC; Practice: Auth system | Auth system |
| Data governance | Data residency, compliance; Practice: Governance framework | Governance doc |
| Audit logging | Track all interactions; Practice: Audit log | Audit system |
| Disaster recovery | Backup, failover; Practice: DR plan | DR strategy |
| Security best practices | Secrets, encryption, hardening; Practice: Security audit | Security checklist |
Enterprise Tools:
- Auth0, Okta for auth
- Vault for secrets
- Encryption at rest/transit
- SOC2 compliance
Phase 4 Checkpoint: Can you build advanced agentic systems? Can you optimize for production? Can you handle enterprise requirements?
Phase 5: Specialized Use Cases
Conversational AI & Chatbots
| Topic | Details | Deliverable |
|---|---|---|
| Chatbot design | Conversation flows, personality; Practice: Chatbot design | Chatbot system |
| Multi-turn conversations | Context management; Practice: Multi-turn bot | Conversation bot |
| Intent recognition | Classify user intents; Practice: Intent classifier | Intent system |
| Slot filling | Extract entities; Practice: Slot filler | Entity extraction |
| Dialog management | State machines, flows; Practice: Dialog manager | Dialog system |
| Complete chatbot | Production-ready chatbot; Practice: Full chatbot | Production chatbot |
Use Cases:
- Customer support
- Virtual assistants
- Sales chatbots
- Internal help desks
Code Generation & Assistance
| Topic | Details | Deliverable |
|---|---|---|
| Code generation | Generate code from description; Practice: Code generator | Code gen tool |
| Code explanation | Explain existing code; Practice: Code explainer | Code explainer |
| Code review | Automated code review; Practice: Review bot | Review automation |
| Test generation | Generate unit tests; Practice: Test generator | Test gen tool |
| Code refactoring | Suggest improvements; Practice: Refactoring tool | Refactoring assistant |
| Complete coding assistant | Full coding agent; Practice: Coding agent | Coding assistant |
Use Cases:
- GitHub Copilot-style tools
- Code review automation
- Documentation generation
- Test automation
Content Generation
| Topic | Details | Deliverable |
|---|---|---|
| Article writing | Generate long-form content; Practice: Article generator | Content generator |
| Content summarization | Summarize documents; Practice: Summarizer | Summarization tool |
| Style transfer | Match writing style; Practice: Style transfer | Style transfer tool |
| SEO optimization | SEO-friendly content; Practice: SEO optimizer | SEO tool |
| Multi-language | Translation, localization; Practice: Multi-lang system | Translation system |
| Complete content platform | Production content system; Practice: Content platform | Content platform |
Use Cases:
- Marketing content
- Blog posts
- Social media
- Product descriptions
Research & Analysis
| Topic | Details | Deliverable |
|---|---|---|
| Web research agents | Autonomous web research; Practice: Research agent | Research bot |
| Report generation | Synthesize findings; Practice: Report generator | Report system |
| Data analysis | Analyze data with LLMs; Practice: Analysis agent | Analysis tool |
| Literature review | Summarize papers; Practice: Lit review tool | Review tool |
| Competitive analysis | Market research; Practice: Competitive intel | Intel system |
| Complete research assistant | Full research platform; Practice: Research platform | Research platform |
Use Cases:
- Academic research
- Market analysis
- Competitive intelligence
- Investment research
Capstone Project - Production Agentic System
| Topic | Details | Deliverable |
|---|---|---|
| System design | Design multi-agent system; Practice: Architecture | Architecture document |
| Core implementation | Build agent system; Practice: Implementation | Working agent system |
| API & integration | REST API, webhooks; Practice: API development | Production API |
| Monitoring & observability | Full observability stack; Practice: Monitoring | Monitoring system |
| Safety & guardrails | Content moderation, safety; Practice: Safety system | Safety implementation |
| Documentation | Complete docs and runbooks; Practice: Documentation | Full documentation |
Capstone Requirements:
- Multi-agent orchestration
- RAG integration
- Real-time and async processing
- RESTful API with auth
- Full observability (LangSmith/Langfuse)
- Safety guardrails
- Complete documentation
- Docker deployment
- CI/CD pipeline
- Performance benchmarks
Practical Implementation Guide
Essential Tools & Stack
Core Libraries:
# LLM Frameworks
langchain, langchain-openai, langchain-anthropic
llama-index
langgraph, langsmith, langfuse
# Agentic Frameworks
crewai, autogen
# Vector Databases
pinecone-client, qdrant-client, chromadb, pymilvus
# Deployment
fastapi, uvicorn, pydantic
celery, redis # for async tasks
# Observability
langsmith, langfuse, prometheus-client
Infrastructure:
Docker & Docker Compose
PostgreSQL + pgvector
Redis (caching + queues)
Kafka (event streaming)
Prometheus + Grafana
Development:
Git, GitHub Actions
pytest, pytest-asyncio
black, ruff (linting)
pre-commit hooks
Key Engineering Principles
- Start Simple: Begin with basic prompts, add agents as needed
- Measure Everything: Track tokens, latency, costs, quality
- Design for Failure: LLMs are probabilistic - handle failures gracefully
- Version Everything: Prompts, models, agents, configs
- Test Thoroughly: Unit tests, integration tests, eval harnesses
- Monitor Constantly: Observability is critical for LLM apps
- Optimize Iteratively: Make it work, make it right, make it fast
Sample Project Structure
genai-agentic-system/
├── src/
│ ├── agents/ # Agent implementations
│ ├── prompts/ # Prompt templates
│ ├── tools/ # Agent tools
│ ├── rag/ # RAG components
│ ├── api/ # REST API
│ ├── workflows/ # Workflows/chains
│ └── monitoring/ # Observability
├── tests/
│ ├── unit/ # Unit tests
│ ├── integration/ # Integration tests
│ └── evaluation/ # Evaluation datasets
├── docker/ # Docker configs
├── configs/ # Configuration files
├── prompts/ # Prompt registry
├── docs/ # Documentation
└── deploy/ # Deployment scripts
Performance Targets
Latency:
- Streaming response: First token < 500ms
- Simple queries: < 2s total
- Complex agentic workflows: < 30s
- Background tasks: minutes acceptable
Cost:
- Target cost per request (varies by use case)
- Monitor token usage daily
- Set budget alerts
Quality:
- Task success rate: > 85%
- User satisfaction: > 4/5
- Hallucination rate: < 5%
- Safety violations: < 0.1%
Reliability:
- API uptime: > 99.5%
- Graceful degradation on failures
- Circuit breakers for external APIs
Next Steps After Completion
Continue Learning:
- Research Papers: Latest in LLMs, agents, RAG (arXiv)
- Open Source: Contribute to LangChain, LlamaIndex, etc.
- Advanced Topics: Reinforcement learning from human feedback (RLHF)
- Domain Expertise: Specialize in vertical (legal, medical, finance)
Career Paths:
- LLM Engineer / GenAI Engineer
- AI Agent Developer
- Prompt Engineer
- Applied AI Engineer
- AI Product Engineer
Resources
Books:
- “Build a Large Language Model (From Scratch)” by Sebastian Raschka
- “Designing Data-Intensive Applications” by Martin Kleppmann
- “Hands-On Large Language Models” by Jay Alammar & Maarten Grootendorst
Online:
- LangChain Documentation & Cookbook
- LlamaIndex Documentation
- Anthropic Prompt Engineering Guide
- OpenAI Cookbook
- DeepLearning.AI courses (LangChain, LlamaIndex courses)
Communities:
- LangChain Discord
- LlamaIndex Discord
- r/LocalLLaMA, r/LanguageTechnology
- Hugging Face Forums
Papers:
- ReAct: Synergizing Reasoning and Acting
- Chain-of-Thought Prompting
- Retrieval-Augmented Generation (RAG)
- Constitutional AI (Anthropic)
- GPT-4 Technical Report
Practice:
- Build 10 different LLM applications
- Contribute to open-source LLM projects
- Participate in hackathons
- Build portfolio projects