Practical Implementation and Production Systems

This topic introduces agentic architectures where LLMs transcend simple input-output pairs to engage in planning, tool use, and iterative problem-solving. An agent loop typically includes: (1) perception of the current state and available tools, (2) reasoning about how to proceed, (3) action selection such as calling a tool, (4) receiving...

This topic introduces agentic architectures where LLMs transcend simple input-output pairs to engage in planning, tool use, and iterative problem-solving. An agent loop typically includes: (1) perception of the current state and available tools, (2) reasoning about how to proceed, (3) action selection such as calling a tool, (4) receiving feedback on the action, and (5) returning to reasoning with updated context. Learners design prompts that instruct the model to engage in this loop, maintaining a plan or goal stack, explaining reasoning at each step, and deciding when to stop. The topic covers tool selection strategies, including how models determine which tools are relevant and how to design prompts that improve tool selection accuracy. It also addresses failure modes such as infinite loops, tool misuse, and hallucinated tool capabilities, and mitigation strategies including validation and simulation. Common agentic patterns are examined including ReAct (reasoning and acting), Tool-using Language Models, and Hierarchical Planning.

This topic focuses on practical integration where LLMs leverage external tools and data sources not available in their training. It covers API design for LLM consumption, including schemas that models can understand, clear documentation of parameters and return values, and appropriate error handling. Learners design prompts that specify available tools...

This topic focuses on practical integration where LLMs leverage external tools and data sources not available in their training. It covers API design for LLM consumption, including schemas that models can understand, clear documentation of parameters and return values, and appropriate error handling. Learners design prompts that specify available tools and their usage, generate tool calls in structured formats (JSON function calls), and process returned data. The topic addresses challenges including token budget constraints when including API responses, handling tool errors gracefully, and deciding when to retry failed calls versus accepting failure. It also covers authentication and authorization for API calls from LLMs, including how to securely manage credentials without exposing them in prompts or logs. Patterns such as function chaining where one API call's output becomes input to another, and recursive API use where the LLM iteratively gathers information, are explored.

This topic addresses organizational scaling challenges beyond single use cases. It covers creating shared prompt libraries that teams can discover and reuse, establishing naming conventions and tagging systems for easy lookup, and versioning policies that protect users from breaking changes. Learners examine organizational patterns including centralized AI platform teams that...

This topic addresses organizational scaling challenges beyond single use cases. It covers creating shared prompt libraries that teams can discover and reuse, establishing naming conventions and tagging systems for easy lookup, and versioning policies that protect users from breaking changes. Learners examine organizational patterns including centralized AI platform teams that provide shared infrastructure and support, communities of practice where prompt engineers share learnings, and federated approaches where teams retain autonomy while adhering to shared standards. The topic discusses cost optimization at scale including batching requests to reduce API costs, caching frequent queries, and dynamically routing requests to cheaper models when appropriate. It also covers governance at scale including review processes for prompts used in high-stakes applications, documentation requirements, and auditing of AI system use. Learners design onboarding experiences for new prompt engineers joining the organization and knowledge transfer mechanisms.

This topic addresses how to manage prompts as evolving artifacts similar to code in software engineering. It covers storing prompts in version control systems (Git), creating clear commit messages explaining why a prompt was changed, and maintaining branches for experimental variations. The topic discusses how prompt performance evaluation differs from...

This topic addresses how to manage prompts as evolving artifacts similar to code in software engineering. It covers storing prompts in version control systems (Git), creating clear commit messages explaining why a prompt was changed, and maintaining branches for experimental variations. The topic discusses how prompt performance evaluation differs from code testing, as outputs are non-deterministic and require comparison on evaluation sets rather than simple pass/fail unit tests. Learners design workflows including development phases where new prompts are tested locally with small datasets, staging phases where prompts are tested in realistic environments with production-scale data, and production phases where prompts serve real users. The topic covers rollback strategies when production prompts degrade and how to grandfather in old prompts to support existing users during transitions. Learners also examine how to document the rationale for prompt changes, connecting versions to performance improvements or new capabilities.

This topic addresses how to keep production prompt systems healthy and reliable. It covers key metrics including latency (response time), throughput (requests per unit time), cost (spending per request or per outcome), and quality (accuracy, user satisfaction). Learners design logging strategies that capture prompts, models used, outputs, and evaluation results...

This topic addresses how to keep production prompt systems healthy and reliable. It covers key metrics including latency (response time), throughput (requests per unit time), cost (spending per request or per outcome), and quality (accuracy, user satisfaction). Learners design logging strategies that capture prompts, models used, outputs, and evaluation results while respecting privacy constraints. The topic explains how to detect drift where model outputs gradually degrade over time, which can occur due to model updates, changing input distributions, or shifts in ground truth. Learners implement automated evaluations that assess output quality, either through LLM-as-judge approaches or task-specific metrics, triggering alerts when performance falls below thresholds. The topic also covers how to establish baselines for normal system behavior and detect anomalies such as unexpectedly high error rates or unusual output patterns. Dashboards and observability tools are discussed, including how to make system behavior visible to technical and non-technical stakeholders.

This topic emphasizes that knowledge captured through prompt engineering practice should be documented and shared. Learners create prompt documentation including purpose, intended use cases, example inputs and outputs, known limitations, and performance benchmarks. They also document system-level design decisions explaining why certain architectural choices were made, what tradeoffs were accepted,...

This topic emphasizes that knowledge captured through prompt engineering practice should be documented and shared. Learners create prompt documentation including purpose, intended use cases, example inputs and outputs, known limitations, and performance benchmarks. They also document system-level design decisions explaining why certain architectural choices were made, what tradeoffs were accepted, and what alternatives were considered. The topic covers creating internal wikis or knowledge bases where prompt engineers can find existing solutions and contribute new patterns. It also discusses meta-documentation that helps teams understand what they know, what they've tried that failed, and what they still need to learn. Learners examine how to surface documentation effectively through tagging, search, and discovery mechanisms. The topic emphasizes that documentation is not a one-time activity but an evolving artifact that reflects learning as systems mature and new insights emerge.