Who Books This Workshop

The buyer is consistent across industries: someone responsible for engineering capability who has watched their team plateau on AI features and wants to fix it before the next quarter. Five archetypes recur.

• VPs of Engineering at growth-stage companies: the team has shipped two AI features that are working but inconsistent, and the VP wants the next cohort to be built on real discipline
• Heads of L&D and learning leaders at large enterprises: budgeting an annual AI capability uplift for 50 to 500 engineers, looking for a senior practitioner-led workshop instead of vendor-led training
• CTOs and Chief AI Officers at mid-market companies: just hired their first ML engineer, want to bring the rest of the engineering team up to a shared baseline
• Tech leads or platform leads owning the internal AI platform: looking to ship reusable patterns to product teams instead of having every team re-invent retrieval and evaluation
• Engineering directors at consultancies and agencies: training delivery teams across multiple client engagements on a unified LLM stack
• Heads of Product Engineering at AI-native startups: 8 to 30 engineers, the entire team needs to operate at a senior level on the LLM stack within a single sprint

Curriculum

The full curriculum spans the LLM engineering surface. The default sequence below works for a 1-day intensive or a 2-day deep version. Half-day formats pick a subset.

• Prompt design patterns and prompt engineering anti-patterns: structured output, few-shot calibration, role priming, the trade-off between brevity and reliability, version control for prompts
• Evaluation discipline: rubrics, eval set construction, regression testing, LLM-as-judge calibration, golden examples, the difference between offline and online evals
• Retrieval-augmented generation in practice: chunking strategies, hybrid search (BM25 plus dense), reranking, query rewriting, top-k tuning, evaluation of retrieval separately from generation
• When and how to fine-tune (and when not to): LoRA and QLoRA, dataset curation, training run economics, the prompt-engineering vs fine-tuning decision boundary
• Cost, latency, and token budget design: prompt caching, model routing across GPT-5 / Claude Opus 4.7 / Gemini / open weights, batch vs streaming, the 80 percent cost reduction case studies
• Observability and drift detection: trace logging, eval dashboards, regression alerts, what good observability looks like at the application layer (Langfuse, LangSmith, Braintrust, Arize Phoenix)
• Structured output and tool use: function calling, schema design, MCP basics, guardrails for non-deterministic systems
• Production posture: deployment, rollback, A/B testing for prompts and models, model deprecation handling, vendor risk
• Governance and risk: data handling, PII redaction, audit trails, the practical compliance shape for regulated industries

Hands-On Outcomes

The workshop produces artifacts the team owns when it ends. Each artifact is built during the labs against the team real data and real stack, not generic examples.

• A frozen evaluation set built from the team real data: 50 to 200 examples with rubrics, ready to be the regression baseline for every future change
• A working RAG pipeline against the team corpus: ingestion, chunking, indexing, hybrid retrieval, reranker, evaluated end-to-end
• A documented prompt pattern library the team can reuse: structured outputs, few-shot exemplars, role priming patterns, version-controlled
• A model-routing strategy that fits the team cost profile: which model for which class of request, with measured latency and cost numbers
• An observability baseline: trace logging integrated, dashboards stood up, regression alerts wired in
• A short written follow-up document summarizing the decisions the team made during the workshop, the tradeoffs surfaced, and the next four to six weeks of recommended work

Workshop Formats

The right format depends on team size, current competence, and how much time the leadership team can carve out. The four default shapes below cover most engagements.

• Focused 2-hour session: one module deep (e.g. eval discipline only, or RAG only). Best for executive briefings or as a kickoff before a longer engagement
• Half-day intensive (4 hours): three to four modules covered, light hands-on, no full lab build. Best when the team wants vocabulary alignment without a full lab sprint
• Full-day bootcamp (6 to 8 hours): the default. Five to seven modules, real hands-on labs, the artifact set above produced by end of day
• Two-day deep version (12 to 16 hours): full curriculum, deeper labs, group exercises, post-workshop project scoped on day 2 afternoon
• Multi-week cohort: a 4 to 8 week format with one half-day per week, designed for distributed teams that cannot block a full day. Each week ends with assigned project work
• Train-the-trainer: a 2 to 3 day version for internal AI platform teams who will then deliver downstream to the rest of the engineering organization

A Realistic Full-Day Agenda

The agenda below is the default full-day shape. The exact mix is calibrated in a pre-workshop planning call against the team current stack, current features, and current pain points.

• 09:00 to 09:30 - Kickoff: shared baseline, team current stack walkthrough, agreed labs targets
• 09:30 to 11:00 - Module 1: Prompt engineering patterns and anti-patterns, with a 30-minute lab building structured outputs against the team data
• 11:00 to 12:30 - Module 2: Evaluation discipline. Lab: build an eval set from real production traces, write the rubric, score with LLM-as-judge
• 12:30 to 13:30 - Working lunch with a 30-minute open Q&A on whatever the team brings
• 13:30 to 15:00 - Module 3: Retrieval-augmented generation. Lab: stand up a hybrid retriever against the team corpus, evaluate retrieval separately from generation
• 15:00 to 16:00 - Module 4: Cost, latency, and model routing. Lab: build a router with measured cost and latency on the team workload
• 16:00 to 16:45 - Module 5: Observability and drift detection. Lab: wire a tracing tool into the team stack and define the first 5 dashboards
• 16:45 to 17:30 - Wrap: artifact handoff, decision summary, recommended 6-week project plan

Pre-Workshop Preparation

A workshop is only as good as the preparation. The pre-workshop sequence below is what separates a useful 2-day engagement from a glorified vendor demo.

• Planning call (60 minutes): instructor talks with the engineering lead about current stack, current features, current pain points, and the desired post-workshop state
• Pre-read pack: a small set of papers and engineering writeups for the team to skim before day 1 (Anthropic Building Effective Agents, evaluation case studies, retrieval benchmarks)
• Data access: a sample of the team real data (with PII handling) so the labs run against the actual corpus, not toy data
• Stack access: read-only access to the team observability and tracing stack so the labs integrate with what the team already operates
• Pre-survey: 10 minutes per attendee, capturing current confidence on each module and the one question they most want answered
• Logistics setup: video, screen-share, shared lab notebooks, lab cloud credits if labs require GPU time

Post-Workshop Follow-Through

The biggest failure mode of corporate training is that the artifacts and momentum decay within two weeks. The post-workshop sequence below is the standard fix.

• Day 0 follow-up: a written summary of decisions made during the workshop, the artifact set, and the recommended 6-week project plan
• Week 2 check-in (30 to 60 minutes): the instructor returns for a follow-up call on what the team has shipped, what blocked them, and what to adjust
• Week 6 check-in (60 to 90 minutes): deeper review, often combined with a code review of the eval harness and the RAG pipeline the team built
• Optional ongoing advisory retainer: 2 to 4 hours per month after the workshop, often booked by VPs who want to keep the momentum
• Optional follow-up workshop: 3 to 6 months later, calibrated to where the team has progressed and the next layer of capability

Logistics, Fees, and Lead Time

Workshop pricing is day-rate based, not seat-based. The fee reflects instructor seniority, preparation depth, and customization to the team stack rather than headcount.

• Half-day workshop (4 hours, customized): typically $5,000 to $12,000 in the US, plus travel for in-person
• Full-day workshop (6 to 8 hours, customized): typically $10,000 to $25,000 in the US, with the upper end for deeply customized stacks or specialist domains
• Two-day deep workshop: typically $20,000 to $45,000, with full hands-on labs and a written follow-up plan
• Multi-week cohort (4 to 8 sessions): typically $30,000 to $80,000 depending on cadence and group size
• Train-the-trainer: priced higher per day because of the prep depth and the durability of the deliverable
• Virtual delivery: typically priced 20 to 40 percent below in-person, same prep depth, same artifact output, calibrated for the virtual room
• Group size: best between 8 and 25 engineers. Up to 40 is workable with TA support. Above 40 the labs lose their density and the team should split cohorts
• Lead time: 4 to 8 weeks comfortable for a customized full-day workshop. 2 to 4 weeks workable for repeat clients. Under 2 weeks is possible only for off-the-shelf agendas
• Customization premium: 15 to 25 percent for deep stack customization (the labs running against your specific platform, your specific data, your specific observability tools)

Right Fit and Wrong Fit

A workshop is the right answer for a specific class of problem. Knowing when it is not the right answer saves the budget for the work that actually moves the team.

• Right fit: an engineering team that has shipped at least one LLM feature and is plateauing, with a tech lead who wants the team to operate at a senior baseline
• Right fit: a platform team that will then deliver patterns to downstream product teams
• Right fit: an AI-native startup where the entire engineering team needs to share a vocabulary within a single sprint
• Right fit: a regulated industry team that needs to bake evaluation, observability, and audit trails into their first AI deployment
• Wrong fit: a team with no AI features in production yet and no concrete first project. Build the first project first; book the workshop after
• Wrong fit: an audience that wants entertainment rather than capability. Workshops are work, not stage time
• Wrong fit: a team where the leadership wants the workshop to substitute for hiring senior engineers. Workshops level up the team; they do not replace the senior engineer the team is missing

How to Book

Booking is a short structured sequence. The decision typically closes in 7 to 10 business days for workshops more than 4 weeks out.

• Step 1: send a one-page brief: team size, current stack, current AI features in production, the capability gap, target date
• Step 2: 30 to 60 minute alignment call to confirm format, agenda, and learning outcomes
• Step 3: contract issued within 5 business days: fee, scope, format, AV and stack requirements, cancellation terms
• Step 4: planning call 2 to 4 weeks before delivery to lock the customization
• Step 5: deliver. Half-day, full-day, or two-day, in person or virtual
• Step 6: written follow-up plus the week 2 and week 6 check-ins

FAQ