Course Objective & Scope
Calling the largest frontier model with a massive context on every single user action looks great in a demo, but in production it leads to eight-second waits and API bills that wipe out product margins. This course teaches product and engineering strategies to optimise latency and unit cost without sacrificing user value.
Participants examine complexity-based model routing, prompt caching, context trimming, asynchronous batch APIs and perceived-speed UX techniques, producing a concrete cost–latency–value optimisation plan for their product.
Who Should Attend?
- AI product managers (PMs) and technical product leads
- LLMOps, backend and applied AI engineers
- Technology managers accountable for unit economics and API/cloud spend
- Prerequisite: basic understanding of LLM concepts (tokens, context windows, APIs)
Key Takeaways
- Define target latency budgets (TTFT and end-to-end) and maximum cost per task for each feature
- Design model routing and cascading architectures pairing fast small models with frontier reasoning models
- Cut token spend through prompt caching, output conciseness controls and tighter RAG chunking
- Shift non-interactive workloads to asynchronous and batch APIs for major cost savings
- Select models and configurations along the quality–latency–cost Pareto frontier using your own eval set
Curriculum
The Iron Triangle of AI Products: Quality, Latency and Cost
- Input versus output token pricing, hidden reasoning tokens and multi-step agent multipliers
- Mapping features to interaction tiers: sub-second inline, 3-second chat or async background
Model Routing and Cascaded Architectures
- Routing routine requests to mini/flash models or fine-tuned open-weight models
- Escalating to frontier or reasoning models only on complex or low-confidence cases
Context Budgeting and Caching Strategies
- Structuring static prefixes to maximise provider prompt-cache hit rates
- Tuning RAG top-k, trimming conversation history and constraining output verbosity
Perceived Latency UX and Asynchronous Workloads
- Token streaming, progressive UI rendering and optimistic updates
- Pre-computing likely actions and offloading background jobs to discounted Batch APIs
Workshop: Pareto Frontier Analysis and Margin Plan
- Benchmarking candidate models on your eval set across accuracy, p95 latency and cost
- Setting quotas, tier rate limits and cost-anomaly circuit breakers
Other Courses in This Category
-
Managing AI-native products where AI is the core engine rather than a bolted-on feature: compound AI systems, eval-driven development lifecycles, data flywheels and modern PM–AI engineer collaboration.
- Live Course
- 5 modules
-
Agent Experience (AX) and delegation UX for products where users hand off multi-step jobs to autonomous agents: task briefing, plan sign-off, async progress tracking, blocker escalation and deliverable review.
- Live Course
- 5 modules
-
Governing foundation model migrations, prompt edits and tool schema updates in live AI products without regressions: versioning, automated eval gates, canary rollouts and rollback protocols.
- Live Course
- 5 modules
-
AI product strategy for product leaders: value proposition and defensibility (moats), build-versus-buy-versus-partner decisions, token and inference unit economics, pricing models and an actionable AI strategy brief.
- Live Course
- 5 modules
-
Product discovery for probabilistic AI products: identifying AI-suited problems, rapid feasibility spikes on real data, Wizard-of-Oz and prompt prototyping, Minimum Viable Quality (MVQ) and eval-driven acceptance criteria.
- Live Course
- 5 modules
-
AI product roadmap and portfolio management in a fast-moving model landscape: balancing quick wins with strategic bets, avoiding features that next-generation base models will commoditise, and stage-gated delivery.
- Live Course
- 5 modules
Get in touch
