Incline Protocol

AI Data Readiness

Encompassing key areas that fall under Product Management and Product Development.

Data Inventory and Accessibility

  • Know what data exists, where it is, and how to access it.
  • Fragmented or siloed data hampers any AI initiative from the start.

Labeling and Annotation Quality

  • Especially critical for supervised learning or NLP use cases.
  • Poor labels = poor models, regardless of algorithm strength.

Bias, Fairness, and Representativeness Checks

  • Ensure datasets don’t reflect harmful or skewed patterns.
  • Unchecked bias leads to non-compliant, untrustworthy AI.

Data Quality and Consistency

  • AI needs clean, consistent data with minimal noise or missing values.
  • Garbage in, garbage out still applies—especially with ML.

Volume and Temporal Suitability

  • Is there enough historical data to train models?
  • Are patterns likely to persist or drift over time?

Real-Time vs. Batch Strategy

  • Match AI needs to data freshness (real-time recommendations vs. batch scoring).
  • Not all use cases justify streaming architectures.

Data Retention and Versioning Controls

  • Maintain historical snapshots of datasets to allow model re-training or rollback.
  • Enables reproducibility and compliance.

Semantic Consistency Across Sources

  • Standardize definitions and labels (e.g., “customer ID” should mean the same everywhere).
  • Avoids misalignment that ruins model inputs.

Data Governance in a nutshell.

Data Lineage and Provenance Tracking

Know the source and transformation history of datasets used in models.
Essential for auditability and debugging.

Human-in-the-Loop (HITL) Design

Primary Goal:
Ensure that humans can validate, approve, or intervene in AI outputs at critical decision points to minimize risk.

Focus Areas:

  • Embed checkpoints for human approval or edits before AI outputs are finalized or acted upon.
  • Define escalation workflows and override options for risky or uncertain outputs.
  • Design UI/UX for seamless human-AI interaction and explainability.

Why is it Important:

It maintains safety, trust, and regulatory compliance—especially where mistakes are costly or ethically sensitive. It also helps organizations adopt AI gradually, with humans still in control of key decisions.

Analogy:

Like a human co-pilot approving actions before the autopilot executes them.

Feedback & Ground Truth Loops

Primary Goal:
Improve model accuracy and performance over time by capturing user corrections and expert-validated truth (often asynchronously).

Focus Areas:

  • Collect structured feedback on AI outputs (e.g., thumbs up/down, corrections, annotations).
  • Define what counts as ground truth and build mechanisms for experts to verify and log it.
  • Feed this data into model evaluation, retraining, or retrieval improvement cycles.

Why Is It Important:

It enables continuous learning and domain adaptation—especially in fast-changing or highly specialized fields—without requiring full retraining cycles from scratch. It also builds credibility with SMEs who see their input directly improve system performance.

Analogy:

Like grading homework—used to improve the student's (model's) future performance.

HITL & Feedback Loops - Suggested Order

 1. Human-in-the-Loop (HITL) Design

“Start with control and trust.”

Why first?

  • Keeps humans in charge during early adoption.
  • Mitigates risk while the model is still being validated.
  • Helps teams understand how and when AI should assist—not replace—decisions.
  • Builds internal trust in the AI system’s role and limits.

You're testing the AI with safety nets in place

2. Feedback & Ground Truth Loops

“Now improve the model with real-world usage.”

Why second?

  • You’ll have collected user inputs, corrections, and override data from HITL stages.
  • This data becomes a valuable training or evaluation set.
  • Enables iterative refinement: fine-tuning, prompt adjustments, RAG improvements, etc.
  • Fosters a culture of shared ownership between users and AI systems.

You’re learning from every interaction to get better.

MLOps and LLMOps: Which Team Has Ownership?

Product Development Alignment (Primary Owner)

Why: This focuses heavily on the implementation side—automation pipelines, infrastructure scaling, monitoring systems, and compliance mechanisms.

These are technical enablers that ensure the ML/LLM solutions are reliable, scalable, and maintainable—core concerns of engineering and DevOps teams.

Example development responsibilities here:

  • CI/CD for models
  • GPU orchestration
  • Real-time monitoring tools
  • API endpoints and access controls

Product Management Involvement (Cross-functional Contributor)

Why: Product Managers are responsible for defining the requirements, aligning the MLOps/LLMOps infrastructure with business needs, prioritizing tooling investments, and ensuring regulatory alignment.

PMs also:

  • Define acceptance criteria for operational maturity.
  • Help shape policies around usage, access, and performance metrics.
  • Ensure that the infrastructure supports business objectives like scalability, privacy, and model trustworthiness

Are AI Products Managed Differently?

AI Product Management (PM) Framework Design

Primary Goal:
Equip organizations with a structured approach to manage AI products, accounting for the unique complexities of data, models, and uncertainty.

Focus Areas:

  • Define roles and workflows tailored to AI development (e.g., model lifecycle vs. feature lifecycle).
  • Introduce frameworks for handling iterative experimentation, data dependency, and non-deterministic outcomes.
  • Establish governance for model drift, retraining cycles, and validation processes.
  • Create cross-functional alignment between data science, engineering, compliance, and business stakeholders.
  • Build product roadmaps that account for technical unknowns, gradual trust-building, and model evaluation checkpoints.

Why Is It Important:

AI product management isn’t just software with models—it requires navigating ambiguity, ethics, and constantly evolving data. A solid framework ensures product managers can responsibly scale AI without relying on outdated IT paradigms.

Analogy:

Like switching from building bridges to exploring space—both need engineering, but AI PM must account for the unknown and constantly recalibrate based on new data.

Trust & UX in AI Interfaces

Primary Goal:

Design AI-powered interfaces that clearly communicate how and why decisions are made—so users feel confident, informed, and in control.

Focus Areas:

  • Display model confidence scores, sources, or rationale alongside AI outputs.
  • Use design patterns that highlight when AI is suggesting vs. deciding.
  • Provide affordances for users to give feedback, challenge, or correct outputs.
  • Transparently communicate limitations, edge cases, and expected behavior.
  • Tailor UX for different trust levels—e.g., high-risk domains vs. assistive tools.

Why Is It Important:

Users lose trust in AI when outputs feel opaque, arbitrary, or unchallengeable. Thoughtful UX makes AI feel more like a partner than a black box—essential for adoption, compliance, and long-term engagement.

Analogy:

Like a GPS that not only gives directions but also shows the route, traffic, and lets you choose an alternate path—you trust it more when you understand how it thinks.

Prompt and UX Design for LLM Products

Primary Goal:

Design intuitive, resilient user experiences that align prompt engineering with product goals, ensuring reliable and user-friendly interactions with LLMs.

Focus Areas:

  • Integrate prompt templates into product flows to ensure consistency and context awareness.
  • Design conversational UX patterns that guide, clarify, and recover from vague or failed inputs.
  • Build fallback mechanisms (e.g., retrieval-based responses, re-prompts, escalation to humans).
  • Establish guardrails for tone, content boundaries, and error prevention.
  • Continuously test and refine prompts based on user feedback and LLM behavior.

Why Is It Important:

Prompt design directly shapes how LLMs behave—bad prompts lead to confusing or risky outputs. Combined with thoughtful UX, prompt engineering becomes a core lever for aligning AI responses with user intent and business objectives.

Analogy:

Like training a concierge to ask the right questions and offer helpful, safe suggestions—prompt + UX design is how you shape the conversation and keep it on track.