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🤖 AI & Agentic AIIntermediate LevelEvidence-Based Skill Profile

Hands-on Mastery in LLM Engineering

Don't just use LLMs. Engineer reliable, production-ready LLM applications. Master transformer architectures, KV-caching, SSE token streaming, token budgeting, strict JSON schema structured outputs, embedding models, hybrid RAG with reranking, LLM-as-a-judge evaluation, OpenTelemetry observability, security guardrails, and fine-tuning with your dedicated in-course LLM Engineering Mastery Agent coach.

12 Modules • 6 Production Projects
12 Modules
AI FearFilter Faculty

What You Will Learn

Master transformer decoder internals, KV-caching mechanics, and inference optimization.
Build low-latency streaming applications with Server-Sent Events and automated token budgeting.
Guarantee deterministic structured outputs with strict JSON Schema and Pydantic self-healing.
Design enterprise vector retrieval architectures with hybrid dense/sparse search and cross-encoder reranking.
Implement automated LLM evaluation pipelines scoring faithfulness, context relevance, and answer accuracy.
Instrument end-to-end LLM observability using OpenTelemetry for trace spans, token counts, and latency tracking.
Secure LLM applications against prompt injections, data extraction, and unsafe completions using guardrails.
Build 6 production-grade LLM projects and achieve verified competency in the Module 12 Capstone Challenge.

Curriculum & Weekly Roadmap

12 Structured Modules

Module 1 — LLM Foundations & Model Architecture

  • Transformers, Attention Mechanisms & Tokenization Internals
  • Model Families, Context Windows & Parameter Scaling
  • Inference Mechanics: Greedy, Temperature, Top-p & KV-Caching

Module 2 — Prompt Engineering & In-Context Learning

  • Zero-shot, Few-shot & Chain-of-Thought Prompt Design
  • System Prompting, Role Calibration & Formatting Constraints
  • Mitigating Ambiguity & Preventing Hallucinations

Module 3 — Structured Outputs & Schema Enforcement

  • JSON Mode & Strict JSON Schema Enforcement
  • Pydantic Integration & Automatic Output Parsing
  • Error Recovery, Self-Correction & Auto-Healing Retries

Module 4 — Function Calling & Tool Execution

  • Tool Calling Protocol & Dynamic Schema Registration
  • Multi-Tool Execution, Parallel Invocations & Error Handling
  • Safe Execution Boundaries & Sandboxing External Tools

Module 5 — Embedding Models & Semantic Search

  • Vector Embeddings, Distance Metrics & Dimensionality
  • Text Chunking Strategies: Fixed, Recursive & Semantic
  • Embedding Model Benchmarking: MTEB, Latency & Cost Trade-offs

Module 6 — Vector Databases & Indexing Strategies

  • Vector DB Architecture: Qdrant, Chroma, Pinecone & pgvector
  • Indexing Algorithms: HNSW, IVF-PQ & Trade-offs
  • Metadata Filtering, Hybrid Namespaces & Multi-Tenancy

Module 7 — Retrieval-Augmented Generation (RAG) Systems

  • End-to-End RAG Architecture: Ingestion, Retrieval & Synthesis
  • Advanced Query Transformations: Multi-Query, Step-Back & HyDE
  • Hybrid Retrieval: Dense + BM25 with Reciprocal Rank Fusion & Reranking

Module 8 — LLM Evaluation & Benchmarking

  • LLM-as-a-Judge: Automated Faithfulness & Answer Relevance
  • Evaluation Frameworks: Ragas, DeepEval & Custom Test Suites
  • Benchmarking Latency, Cost, Token Efficiency & Regression Testing

Module 9 — Observability, Tracing & Monitoring

  • OpenTelemetry Tracing: Tracking Spans, Latency & TTFT
  • Cost & Token Analytics: Budget Alerts & Usage Dashboards
  • Semantic Caching: Redis & GPTCache to Reduce Latency and Costs

Module 10 — Security, Safety & Guardrails

  • Prompt Injection Defense, Jailbreak Mitigation & Data Leakage
  • Input/Output Guardrails: NeMo Guardrails & Llama-Guard
  • PII Redaction, Content Moderation & Toxic Output Filtering

Module 11 — Fine-Tuning & Model Customization

  • When to Fine-Tune vs RAG vs In-Context Learning
  • Parameter-Efficient Fine-Tuning: LoRA, QLoRA & PEFT
  • Dataset Preparation, Format Curation & Alignment

Module 12 — Autonomous Production LLM Capstone Challenge

  • End-to-End Resilient LLM Production Blueprint & Specifications
  • Implementation: Streaming RAG with Structured Outputs & Guardrails
  • Evaluation: Rubric-Based Scoring via LLM Engineering Mastery Agent

Who This Course Is For

AI Engineers, Full-Stack Developers, Backend Engineers, Machine Learning Practitioners, and Technical Architects engineering production LLM systems.

Key Skills Developed:

LLM Architecture & Model InternalsTokenomics, Latency & StreamingProduction Prompt EngineeringStructured Outputs & JSON SchemaEmbedding Models & Vector DatabasesAdvanced RAG & Knowledge RetrievalHybrid Search & RerankingLLM Evaluation & BenchmarkingLLM Observability & TracingLLM Security & GuardrailsModel Selection & Fine-TuningAutonomous Production Capstone Challenge

Course Faculty & Development

AI FearFilter Faculty

LLM Engineering & Foundation Model Team

AI FearFilter Academy

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