Feedback
Feedback is information about an action, system, or outcome that can inform evaluation, decisions, or subsequent behavior.
Lead Developer Career Guide
Information about performance, contributions, or progress that is communicated to reinforce strengths or guide improvement.
- How it is used
- Guides individual growth, team performance, documentation revisions, and decisions about development processes and products.
- How deep it goes
- Develops practical tactics for giving, receiving, and planning feedback conversations, including specificity, setting, listening, and cultural differences.
- Examples
- Uses missed deadlines, specific praise, presentation criticism, and documentation-reader questions to make feedback concrete.
- What you can do after
- Practice/perform specific feedback conversations, solicit perspectives from varied stakeholders, and use the responses to guide improvement.
Think Like a Software Engineering Manager
A feedback mechanism is a process or structure for collecting information about experiences, performance, or conditions.
- How it is used
- Uses feedback to support employee growth, manage performance, improve leadership, and respond to team concerns.
- How deep it goes
- Develops practical channels and conversation habits, from check-ins and surveys to data-backed reviews and follow-up, without prescribing one universal script.
- Examples
- Uses a working-hours policy, a missed project milestone, and periodic employee surveys to show different feedback loops.
- What you can do after
- Practice/perform two-way feedback by gathering specific input, discussing it constructively, acting on it, and checking progress.
Read how Think Like a Software Engineering Manager explains feedback →
"Looks Good to Me"
Feedback is information or opinions shared to help another person or team evaluate and improve work.
- How it is used
- Improves code reviews by making concerns actionable while supporting trust, shared learning, and maintainable code.
- How deep it goes
- Explains comment quality, team expectations, feedback culture, real-time collaboration, and AI assistance without reducing judgment to automation.
- Examples
- Uses vague and constructive PR comments, inconsistent reviewer detail, pairing sessions, mob sessions, and AI review tools as examples.
- What you can do after
- Apply it via code review by writing focused comments, agreeing on response expectations, and choosing human or automated feedback deliberately.
The Art of AI Product Development
User feedback is information provided by users about their needs, experiences, problems, preferences, or reactions to a product.
- How it is used
- Uses feedback to discover unmet needs and improve AI usability, performance, and adoption after launch.
- How deep it goes
- Distinguishes implicit from explicit feedback and gives collection and participation practices, without prescribing a statistical analysis method.
- Examples
- Uses logged edits, thumbs-up/down controls, post-task prompts, and reason-coded forecast reports as feedback artifacts.
- What you can do after
- Practice/perform a feedback loop that captures reactions in context and routes actionable findings into product and model improvements.
Read how The Art of AI Product Development explains feedback →
Design for Developers
Feedback is information communicated to users about the effects or status of their interactions with a product.
- How it is used
- Guides iterative design decisions and communicates interaction results or system status during web tasks.
- How deep it goes
- Develops feedback through a design cycle, interview practices, prioritization guidance, and concrete interaction and status cues.
- Examples
- Uses late application testing, static-design interviews, failed password entry, loading pages, and payment submission.
- What you can do after
- Apply it by testing user-facing work, prioritizing recurring findings, and adding clear interaction, error, loading, and progress cues.
Everyday Data Visualization
Feedback is information or evaluation provided about a product or process to guide its improvement.
- How it is used
- Guides interactive visualization responses and iterative dashboard development toward user and stakeholder needs.
- How deep it goes
- Explains visual response choices and repeated review cycles, while stopping short of tool-specific implementation steps.
- Examples
- Uses dashboard filters and toggles, animated charts and spinners, labels and highlights, prototypes, wireframes, and mockups.
- What you can do after
- Apply it via visible interaction cues and repeated stakeholder and user review.
Shipping Go: Develop, deliver, discuss, design, and go again
Feedback is information about a product or process obtained from evaluation, use, or other responses that can guide subsequent changes.
- How it is used
- Guides the next product or team-process change after a small increment reaches users or reviewers.
- How deep it goes
- Explains when to seek responses and how to handle review comments, without prescribing a formal feedback-collection method.
- Examples
- Illustrates feedback with an untested three-week feature, a staged Hello API demo, and pull-request discussions.
- What you can do after
- Practice/perform it by releasing a small increment, seeking responses, and revising the product or team process.
Read how Shipping Go: Develop, deliver, discuss, design, and go again explains feedback →
BDD in Action, Second Edition
Feedback is information about observed results or understanding that guides subsequent decisions, actions, or improvements.
- How it is used
- Guides requirements discovery, feature prioritization, stakeholder collaboration, and assessment of automated acceptance-test health.
- How deep it goes
- Explains feedback through early demonstrations, conversations, examples, and reliable acceptance tests, without specifying one universal feedback method.
- Examples
- Uses early feature demonstrations, stakeholder conversations, password-strength examples, and automated acceptance-test failures.
- What you can do after
- Apply feedback to revise requirements, prioritize valuable features, refine acceptance criteria, and investigate unreliable automated tests.
Delivering Machine Learning Projects: From design to deployment
Feedback is information, opinions, or evaluations provided about work, behavior, or outcomes to support assessment and improvement.
- How it is used
- Uses feedback to expose production problems and guide post-project improvement.
- How deep it goes
- Explains collection channels, participation methods, review forms, presentation, and investigation, but stops short of formal feedback analytics.
- Examples
- Uses confidential user complaints about login timeouts and a rated team post-project review form.
- What you can do after
- Operate feedback channels and review practices to diagnose production defects and select improvements.
Read how Delivering Machine Learning Projects: From design to deployment explains feedback →
Interview Speak
Information about a person's work or behavior that can guide improvement.
- How it is used
- Uses feedback to assess whether someone accepts criticism, learns from it, and changes behavior.
- How deep it goes
- Explains selection, neutral description, nondefensive reaction, behavioral change, and present-day application.
- Examples
- Uses a perfectionism criticism, reflective answer revision, and a manager acting on team requests.
- What you can do after
- Apply feedback by describing criticism clearly, responding openly, and demonstrating specific changes in future behavior.
Coding with AI
User feedback is information, opinions, or evaluations provided by users about a product, service, or interaction.
- How it is used
- Uses acceptance, review, and ratings to evaluate AI-generated coding suggestions and improve future assistance.
- How deep it goes
- Explains feedback through an evaluation loop and demonstrates review, thumbs-up, and thumbs-down controls.
- Examples
- Uses musician applause or tomatoes, accepted Copilot edits, and a Cursor result modifying river_run.py.
- What you can do after
- Apply feedback by reviewing proposed changes and rating AI coding suggestions before retaining or copying them.
Think Like a CTO
Information about a person's behavior or performance provided to support understanding or improvement.
- How it is used
- Uses feedback to improve executive performance, align technology with business needs, and guide software release and product decisions.
- How deep it goes
- Explains direct questions, team pulse checks, and managed user input, but stops short of defining a formal measurement framework.
- Examples
- Uses client and user input, CEO conversations, team pulse checks, and The Office’s out-of-touch boss as teaching examples.
- What you can do after
- Practice/perform it by requesting candid reviews, setting improvement goals, checking team needs, and managing external input.
AI-Powered Search
User feedback is information supplied directly or indirectly by a user about the quality, usefulness, or correctness of a system response.
- How it is used
- Improves personalized search and recommendations by adapting ranking or relevance to explicit judgments, behavior, and live interaction.
- How deep it goes
- Explains explicit and implicit collection, profile updates, feedback loops, and safeguards, but stops short of implementation algorithms.
- Examples
- Uses thumbs-up or thumbs-down judgments, clicks, purchases, newer interactions, and live conversational response refinement.
- What you can do after
- Apply feedback through explicit controls or behavioral signals to update personalization while limiting errors and respecting user choice.
LLMs in Production
Human feedback is an evaluation, preference, or correction supplied by people about a system's behavior or output.
- How it is used
- Uses human preferences to steer language-model outputs toward acceptable behavior.
- How deep it goes
- Explains response ranking, reward modeling, PPO, costs, privacy, bias, and degradation risks without detailing implementation.
- Examples
- Uses preferred answers to prompts, backflip attempts, and changing prime-number performance as examples.
- What you can do after
- Evaluate generated responses with human preferences and recognize the operational risks of using that feedback for updates.
Effective Conversational AI
User feedback is information provided by users about their experiences, needs, or evaluations of a software system.
- How it is used
- Use direct reports to uncover chatbot pain points and measure whether answers or experiences meet user needs.
- How deep it goes
- Collect and categorize survey responses, pair them with transcripts, and scope ratings, while cautioning against acting on isolated reports.
- Examples
- Uses survey reports about vaccine eligibility and appointment scheduling, plus in-product thumbs-up or thumbs-down ratings.
- What you can do after
- Evaluate chatbot understanding by gathering purpose-specific ratings and investigating recurring user-reported problems.
Build an AI Agent (From Scratch)
End-user feedback is information about system quality or satisfaction provided by people who interact with the deployed system.
- How it is used
- Uses information from deployed-system users as one input for evaluating agent quality and satisfaction.
- How deep it goes
- Defines feedback as an evaluator category but does not specify collection procedures, metrics, or implementation details.
- What you can do after
- Evaluate deployed-system quality or satisfaction using information supplied by interacting users.
Read how Build an AI Agent (From Scratch) explains feedback →
Effective Platform Engineering
Customer feedback is information provided by customers about their needs, experiences, problems, or responses to a product or service.
- How it is used
- Guides platform prioritization, continuous improvement, roadmap decisions, and validation of developer experience.
- How deep it goes
- Explains capture, triage, behavioral comparison, roadmap prioritization, incremental delivery, and measurement without detailing collection instruments.
- Examples
- Uses request queues, Feature Request boards, observed-versus-stated usage, roadmaps, feedback loops, and developer satisfaction measures.
- What you can do after
- Apply feedback through prioritized roadmaps, validated increments, and measurable platform improvements.
Building LLM Applications with DSPy
Text returned by an evaluation metric that explains why a response received its score.
- How it is used
- Uses explanatory metric feedback to help reflective optimizers learn patterns and improve prompts.
- How deep it goes
- Defines feedback, contrasts it with scores, and illustrates its use in an optimizer metric.
- Examples
- Illustrates feedback with uppercase, 10-word output rules and a DSPy metric returning overlap scores and explanations.
- What you can do after
- Apply it via DSPy metric functions to return explanatory feedback alongside a score for SIMBA.
Read how Building LLM Applications with DSPy explains feedback →
Related concepts
- criticismincludes · Criticism is an evaluative judgment or communication that identifies perceived faults, deficiencies, or undesired qualities in a subject, work, behavior, or result.
- reinforcement learning with human feedbackused by · Reinforcement learning with human feedback is a model-training approach that uses human evaluations or preferences to guide optimization of model behavior.
- loopused by · A loop is a programming control-flow construct that repeatedly executes a block of instructions according to an iteration rule or continuation condition.
- reviewerproduced by · A reviewer is a person who examines an artifact, claim, or proposed change against relevant criteria and provides feedback or an assessment.
- software architectused by · A software architect is a technical professional who guides high-level decisions about a software system’s structure, boundaries, and technical direction.
- interviewproduced by · An interview is a structured or semi-structured conversation in which questions elicit information about a participant’s experiences, views, needs, qualifications, or requirements.
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