From transformer internals to full RAG, tool-using agents, Model Context Protocol (MCP), LLMOps, and defensive security. 59 focused topics across 9 production tracks — built for engineers who need to ship real AI systems and defend architectural trade-offs.
"Most courses teach you to call an API. This one teaches you what happens when it fails."
No magical black boxes. Deep dive into transformer layers, self-attention variants, tokenization bugs, quantization mathematics, and positional encodings.
End-to-end implementation of production RAG with hybrid search, re-rankers, LangGraph cyclic state loops, and custom Anthropic Model Context Protocol (MCP) servers.
Hardening for real production. LLMOps telemetry, LLM-as-a-judge calibration, indirect prompt injection defenses, streaming latency, and circuit breaker recovery.
Bridging the critical gap between simple demo apps and resilient, enterprise-scale AI architecture.
115 intensive study hours meticulously divided into 9 practical engineering modules.
Open the black box. Know why models behave the way they do.
Know when to fine-tune, when not to, and how to do it efficiently.
The workhorse of enterprise GenAI. Architecture, retrieval, re-ranking, and advanced failure recovery.
From passive answering to autonomous reasoning, planning, and task execution.
The open standard for connecting LLMs to data, APIs, and tools. Built by Anthropic.
You cannot improve what you cannot measure. Production telemetry and guardrails.
Protect your systems, data, and users from adversarial attacks and toxic failure.
Architecting large-scale, fault-tolerant, resilient enterprise AI systems.
Turn deep knowledge into answers that land top engineering offers.
How every topic in this programme is mastered to guarantee permanent retention and production readiness.
Prioritize the 53 high-priority topics first before touching optional edge cases.
Explain the architecture out loud in simple terms before writing any line of code.
Score your understanding honestly from 1 to 5 after completing hands-on labs.
Anything rated 3 or below is targeted with specific re-drills until thoroughly internalized.
Defend architectural trade-offs under simulated senior engineering scrutiny.
53 high-priority topics are the bedrock. Do not get distracted by niche research papers until your core foundation is ironclad.
Knowing why to choose RAG over fine-tuning or bi-encoders over cross-encoders is 10x more valuable than memorizing API syntax.
Every system breaks. Handling hallucination, stale vector stores, provider outages, and timeouts is taught as a first-class citizen.
Master the geometric and algorithmic intuition before getting lost in mathematical formulas. Code speaks louder than proofs.
Search any topic, filter by track, and explore every single module included in the curriculum.
Open the black box. Know why models behave the way they do.
Encoders vs decoders, attention blocks, residuals, FFNs and layer norm.
Q, K, V matrices, scaled dot-product, multi-head splitting and merging.
Multi-Query Attention (MQA), Grouped-Query Attention (GQA), FlashAttention (why it matters for serving).
Sinusoidal, learned, RoPE (rotary embeddings), ALiBi, and context length extrapolation.
BPE, WordPiece, SentencePiece, byte-fallback, vocabulary size trade-offs and edge-case token bugs.
Next-token prediction, SFT, RLHF, DPO, ORPO, KTO (what happens at each stage).
Greedy, beam search, temperature, top-k, top-p, min-p, repetition penalty.
FP32 vs FP16 vs BF16 vs INT8 vs INT4; weight-only vs activation quantization (AWQ, GPTQ, GGUF).
Know when to fine-tune, when not to, and how to do it efficiently.
When each is appropriate, memory math, resource requirements.
Low-rank decomposition, rank r, alpha, target modules, memory savings in practice.
Data curation, deduplication, synthetic data generation, formatting (ChatML, Alpaca), quality filtering.
Loss curves, benchmark evaluation, domain degradation detection.
The workhorse of enterprise GenAI. Architecture, retrieval, re-ranking, and advanced failure recovery.
Retrieve-then-generate, lost-in-the-middle, retrieval failures, context stuffing issues.
Fixed-size, sentence-level, semantic chunking, recursive character, document-aware (markdown/HTML/PDF).
Dense embeddings, dimensional trade-offs, MTEB benchmarks, fine-tuning embeddings (contrastive loss).
BM25 / TF-IDF vs vector embeddings, lexical vs semantic trade-offs.
Combining dense and sparse, RRF scoring, alpha tuning.
HNSW, IVF, flat index; distance metrics (cosine, dot product, L2); recall vs latency trade-offs.
Cross-encoders vs bi-encoders, Cohere rerank, BGE reranker; latency vs precision balance.
Extracting only relevant sentences, hierarchical indices, parent-child chunking.
Step-back prompting, HyDE (Hypothetical Document Embeddings), query rewriting.
Knowledge graphs + vector search, entity-relationship extraction, multi-hop reasoning over complex data.
RAGAS, TruLens; context precision, context recall, faithfulness, answer relevance metrics.
Adaptive retrieval, fallback to web search, self-reflection tokens, conditional retrieval.
Image embeddings (CLIP, SigLIP), tabular data extraction, PDF parsing pipelines (Unstructured, Marker).
From passive answering to autonomous reasoning, planning, and task execution.
ReAct (Reason + Act), Plan-and-Solve, Reflexion, self-consistency.
OpenAI function calling format, JSON Schema validation, tool selection strategies, error handling on tool failure.
Short-term (working context), long-term (vector/DB backed), entity memory, conversation summarization strategies.
Hierarchical (manager-worker), sequential, peer-to-peer; handoffs, shared state, conflict resolution.
State graphs, nodes, edges, conditional routing, cyclic execution, persistence/checkpoints.
Approval gates, breakpoint execution, state modification mid-run, resume semantics.
Infinite tool loops, hallucinated arguments, runaway token costs, max iteration boundaries.
Sandboxing (Docker, WASM, e2b), safe tool design, code interpreter architectures.
The open standard for connecting LLMs to data, APIs, and tools. Built by Anthropic.
The N*M integration problem, architecture overview (host, client, server), comparison to OpenAI actions.
Resources (data/files), Prompts (templates), Tools (executable functions); schemas, capability negotiation.
STDIO vs SSE transports, JSON-RPC 2.0 messages, creating custom tools and resources with Python/TypeScript SDKs.
Transport security, authentication, permission models, sandboxing MCP servers, enterprise readiness.
You cannot improve what you cannot measure. Production telemetry and guardrails.
Deterministic evals (regex, exact match), LLM-as-a-judge, human evals; building evaluation datasets.
Pairwise vs point-wise, position bias, length bias, self-enhancement bias; calibrated rubrics and few-shot calibration.
OpenTelemetry for LLMs, Langfuse / Arize Phoenix / LangSmith; trace spans, token counts, latency breakdowns.
Prompts as code, git-based workflows, prompt registries, A/B testing prompts in production.
TTFT (Time to First Token) vs ITL (Inter-Token Latency); streaming responses, prompt caching (Anthropic/OpenAI), speculative decoding.
Token accounting, model routing (cheap/fast model first, fallback to frontier), semantic caching (GPTCache), input compression.
Instructor, Outlines, Pydantic, JSON mode vs grammar-constrained generation (why constrained decoding is faster).
Automated eval runs on PR, regression testing, cost budgets, golden test sets.
vLLM, Ollama, TGI; PagedAttention, continuous batching, tensor parallelism basics.
Capturing user feedback (implicit/explicit), using production traces to build eval sets and fine-tuning data.
Protect your systems, data, and users from adversarial attacks and toxic failure.
Direct injection (jailbreaks), indirect prompt injection (via retrieved data), defense in depth.
Input vs output guardrails, NeMo Guardrails, Llama Guard, Guardrails AI; regex vs embedding vs classifier vs LLM checks.
PII detection and redaction (Presidio), zero-data-retention APIs, compliance boundaries (GDPR, HIPAA in AI systems).
Faithfulness checking, citation generation, ground truth attribution, self-verification passes.
Insecure output handling, model theft, training data poisoning, supply chain vulnerabilities.
Architecting large-scale, fault-tolerant, resilient enterprise AI systems.
Ingestion pipeline, hybrid search, rerank, generation, streaming, caching, access control (ACLs on chunks).
Repository indexing, tool design, multi-file edits, test-run loops, error correction, context management.
Token bucket, TPM/RPM limits across providers, fallback queues (Celery/Redis), graceful degradation.
Hosted vs open models on quality, latency, cost, context window, and data privacy.
Timeouts, exponential retries, partial failure, stale indexes, provider outages, circuit breakers, rollback.
Turn deep knowledge into answers that land top engineering offers.
Problem definition, architecture, trade-offs, metrics, failure modes encountered and what you would change.
Defend choices on RAG vs fine-tuning, model size vs cost, latency vs accuracy, safety vs velocity.
Applications are reviewed on a rolling basis. Once your seat is reserved, our admissions advisor will share batch schedules, environment setup guides, and prerequisites.
Fill out the application form below. Our engineering admissions team will get in touch with you within 24 hours.
Everything you need to know about the curriculum, time commitments, and requirements.
Reserve your seat in the upcoming 2026 cohort or download the complete 10-page engineering curriculum syllabus today.
