211 minutesDeep Dive into LLMs like ChatGPT
Understand the modern LLM stack well enough to reason about tokens, training, tools and failures.
Production depth. System design, evals, and the hard architectural calls.
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211 minutesUnderstand the modern LLM stack well enough to reason about tokens, training, tools and failures.
40 minutesEvaluate partial-autonomy product ideas where humans keep control over high-risk decisions.
190 minutesYou can build a LangGraph agent with typed state, conditional routing, checkpoints and tool use, and feel where the explicitness pays off.
66 minutesYou can stand up a CrewAI crew in the role-goal-backstory style and judge how its hidden execution loop compares with explicit graphs.
15 minutesDesign simpler agent loops with clear stopping rules, task boundaries and human control points.
18 minutesRecognize common agent-building pitfalls before adding multiple agents, complex prompts or hidden state.
17 minutesSet up a multi-agent coding workflow with explicit review boundaries so generated changes stay small, tested and owned.
56 minutesBuild an ROI case for one AI workflow with a baseline, direct and indirect benefits, quality gates and an explicit stop decision.
41 minutesMake AI build-vs-buy decisions around outcome, data control, workload economics, infrastructure readiness and operational ownership.
20 minutesEvaluate practical access-control patterns for company knowledge RAG before indexing sensitive internal documents.
5 minutesUnderstand why screenshot-based computer use is powerful, slow, expensive and brittle compared with API-native automation.
8 minutesDecide where browser or computer-use agents might be commercially useful despite their operational risk.
22 minutesApply write, select, compress and isolate patterns to manage agent context deliberately.
8 minutesUnderstand how long context can fail under ambiguity and distractors, then design tests around that risk.
19 minutesYou can estimate when prompt caching pays off by weighing cache-write surcharges against read savings for your real workloads.
56 minutesUse prompt caching only when stable prefixes, latency and cost behavior match the workload.
30 minutesMap the Act's phased 2025-2027 deadlines to the vendor-evidence and ownership tasks an SME must schedule now.
55 minutesDesign an eval ladder that catches regressions before prompt or model changes reach users.
19 minutesSee how a production team layers assertions, human review and LLM judges so regressions surface before release.
157 minutesYou will be able to run quantisation, LoRA and QLoRA fine-tunes end to end on hardware most developers already have.
59 minutesPlace fine-tuning inside the broader training pipeline and decide when it is better than prompting or RAG.
104 minutesUnderstand how MCP fits into agentic systems beyond a local demo server.
75 minutesBuild a first MCP server and understand how tools, schemas, prompts, resources and transports fit together.
19 minutesImprove tool design so agents select the right action with the right parameters.
29 minutesDecide which behavior belongs in the system prompt, tool description or tool precondition.
7 minutesSeparate short-term and long-term memory decisions and decide when agent memory should be written.
44 minutesYou can wire episodic, semantic, working and procedural memory into an agent in code, not just name the categories.
9 minutesYou can navigate traces, projects and datasets in LangSmith and read off token cost, latency, error rate and per-span detail.
154 minutesConnect tracing, evaluation, feedback and production review into an operating loop for LLM systems.
37 minutesEvaluate private AI as an infrastructure and governance decision instead of defaulting to either SaaS or self-hosting by instinct.
69 minutesRecognize the failure modes your own agents will hit first and pre-plan the tests, fallbacks and kill criteria for each.
17 minutesUnderstand what breaks when RAG moves into regulated, high-stakes enterprise use.
19 minutesIdentify the production RAG controls missing from naive document-chat demos.
13 minutesModel prompt injection as untrusted-data mixing and design boundaries around tool use.
17 minutesReview prompt-injection defenses such as taint analysis, output-shape restrictions, user isolation, deterministic settings and redundant checks for critical paths.
9 minutesExplain when retrieval is the right fix and when fine-tuning may actually help.
13 minutesChoose between prompt engineering, RAG, fine-tuning or a combination based on the actual failure mode.
77 minutesHear how production prompt engineers revise instructions, examples and behavioral constraints under real pressure instead of treating prompts as one-off text.
25 minutesWatch a vague insurance-claims prompt become a structured, testable prompt with clearer examples, output rules and refusal behavior.
39 minutesCompare agentic retrieval and graph retrieval in a concrete implementation.
16 minutesUnderstand the Microsoft-style GraphRAG flow: entity extraction, communities, summaries and query-focused synthesis.
7 minutesUnderstand why document parsing, structure preservation and ingestion quality gates matter before building RAG over PDFs and mixed file formats.
32 minutesUnderstand why serving engines, batching and KV-cache memory dominate self-hosted inference economics.
42 minutesAssess when vertical AI agents create real defensibility and when they are only thin wrappers.
34 minutesEvaluate AI product pricing and specialization around measurable outcomes rather than seat counts.
41 minutesYou can explain what strict mode guarantees, how it differs from old JSON mode, and how refusal handling fits the structured-output contract.
18 minutesLearn why schema-first LLM calls need typed objects, validators, retries and explicit handling for malformed or hallucinated fields.
6 minutesRecognize the core architecture of a voice agent and the failure points that affect customer trust in real calls.