AI Prompt Engineering (Nelson Ming)


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#promptengineering #generativeAI #LLMtesting #agenticAI #promptevaluation #AIPromptEngineering


These are takeaways from this book.


Firstly, Prompt engineering as an interface discipline, A central theme is treating prompts as an interface layer between humans, business intent, and probabilistic model behavior. Instead of viewing a prompt as a single instruction, the book positions it as a designed artifact that includes roles, task framing, constraints, and success criteria. This perspective helps teams reason about why a model output is acceptable or not, and which part of the prompt contract needs adjustment. It also encourages clarity about the operating context: what the model should assume, what it must ask for, and what it must never do. By focusing on interface design, readers can move from ad hoc prompting to repeatable structures that can be reviewed, versioned, and maintained like other software components. The discussion naturally connects to product requirements, where you define the user journey, expected outputs, and acceptable error bounds. This topic also covers how prompt structure influences tone, specificity, and safety. When prompts are treated as interfaces, you can intentionally design for robustness across varied inputs, reduce ambiguity, and create outputs that integrate smoothly with downstream systems such as databases, UIs, or reporting pipelines.


Secondly, Core prompt patterns for controllable outputs, The book highlights practical prompt patterns that increase controllability and reduce variance in model outputs. These patterns commonly include clear task definition, explicit output formats, boundary conditions, and stepwise workflows that guide the model through analysis to deliverables. The emphasis is not on gimmicks but on durable techniques that generalize across use cases like summarization, classification, extraction, drafting, and transformation. Readers learn to specify inputs and outputs precisely, such as demanding structured fields, lists, or JSON like schemas for integration into applications. Another pattern is providing criteria for quality, such as completeness checks, coverage requirements, and disallowed content. The book also explores how to include examples or reference structures without making the model overly rigid, supporting both creativity and compliance. These patterns help reduce the need for repeated back and forth with the model and improve consistency among different users. The overall goal is to create prompts that behave predictably under production conditions, where there is no time to manually fix responses. By emphasizing patterns, the book gives readers a toolkit for building prompts that are testable, auditable, and easier to iterate.


Thirdly, Building agentic workflows and tool use, A distinguishing focus is applying prompt engineering to agentic AI, where a model is expected to plan, act, and coordinate tasks rather than only respond. This topic covers how prompts can define an agent role, objectives, and operating rules, as well as how the model should decide when to use tools, request clarification, or stop. Agentic systems introduce new failure modes such as looping, tool misuse, and brittle planning, so the book frames prompts as guardrails that keep actions aligned with user intent. It also addresses decomposition: breaking complex goals into manageable steps, tracking intermediate results, and maintaining a reliable task state. In real applications, agents often interact with external systems like search, code execution, ticketing tools, or databases. Prompt design becomes critical for specifying tool schemas, handling tool errors, and ensuring outputs are grounded in retrieved information. This topic also includes coordination patterns, such as a manager worker structure or specialized sub agents for research, writing, and verification. The result is a blueprint for moving from chat style prompting to orchestrated workflows that can deliver repeatable outcomes.


Fourthly, Testing, evaluation, and quality assurance for prompts, Prompt quality cannot rely on intuition alone, especially as systems scale. The book emphasizes evaluation methods that make prompting measurable, enabling teams to test changes and prevent regressions. This includes building representative test sets, defining acceptance criteria, and tracking metrics such as accuracy, format compliance, completeness, safety adherence, and user satisfaction. It also highlights the importance of evaluating across diverse inputs, including edge cases, ambiguous requests, and adversarial attempts to bypass constraints. Because LLM behavior can vary, the topic stresses repeatability techniques like deterministic settings where possible, standardized rubrics, and automated checks for structure and policy compliance. Another key point is separating prompt defects from data defects and model limitations, which helps teams decide whether to adjust instructions, add retrieval grounding, or change the application flow. The book also encourages versioning prompts, running A B comparisons, and documenting prompt intent so that teams can collaborate effectively. By treating prompts as testable artifacts, organizations can improve reliability, reduce production incidents, and align the system with both user needs and governance requirements.


Lastly, Deployment considerations and operational prompt governance, Once a prompt moves into production, operational realities shape how it must be managed. The book addresses deployment level concerns such as prompt version control, environment specific configuration, monitoring, and safe iteration. It explores how prompts interact with system messages, user inputs, and dynamic context like retrieved documents or tool outputs, and why these interactions must be controlled to avoid unpredictable behavior. Governance also matters: prompts can encode policy, compliance rules, and brand tone, so teams need review workflows and audit trails. The topic includes strategies for minimizing prompt injection risk by isolating untrusted text, validating outputs, and enforcing strict formatting for downstream processing. It also covers cost and latency tradeoffs, since longer prompts and multi step agent loops can increase usage and slow responses. Operational guidance extends to logging and feedback loops, helping teams detect where prompts fail and prioritize improvements. In effect, deployment turns prompt engineering into an ongoing practice similar to maintaining APIs: you must monitor performance, handle changes in model behavior, and keep the prompt aligned with evolving product requirements. This lifecycle view helps readers build systems that remain dependable over time.