IVQA 701-750

701. What are the benefits of using LangGraph for structured agent workflows?

LangGraph is a state-machine-based orchestration tool built on top of LangChain. Benefits include:

  • Structured control flow: Explicit node-based modeling of agent actions and decisions.

  • Memory integration: Native support for storing and retrieving memory across agent steps.

  • Looping and branching: Allows complex workflows (e.g., retry loops, conditional paths).

  • Observability: Built-in tools for visualizing and debugging agent paths.

  • Composable graphs: Enables modular and reusable agent logic.

702. How does an LLM orchestrator differ from a general-purpose workflow engine like Airflow?

Feature
LLM Orchestrator (e.g., LangGraph, AutoGen)
Airflow / Prefect / Dagster

Optimized for LLMs

Yes

No

Handles prompts & tools

Yes

No (needs plugins)

Agent-state management

Built-in (e.g., memory, retries)

Manual

DAG enforcement

Flexible (stateful/looping allowed)

Strict DAG

Use case

Multi-turn reasoning, agents

ETL, data pipelines

703. How would you design a state machine for a GenAI multi-turn process?

A basic state machine could include:

  • States: INIT, FETCH_CONTEXT, GENERATE_RESPONSE, CHECK_COMPLETENESS, FINALIZE.

  • Transitions: Defined by LLM outputs or conditions (e.g., response confidence).

  • Memory: Updated at each step (chat history, intermediate results).

  • Looping: Allowed between GENERATE_RESPONSE ↔ CHECK_COMPLETENESS.

  • Error States: FAILED, CANCELLED with defined recovery transitions.

Example using LangGraph or XState-like syntax.

704. What are the advantages of memory-aware orchestration in GenAI systems?

  • Contextual continuity: Retains chat history, goals, or intermediate results.

  • Personalization: Adapts responses using user-specific or session-based data.

  • Efficiency: Avoids redundant calls by caching and reusing prior outputs.

  • Error handling: Enables retries or fallbacks based on state/memory.

  • Multi-agent collaboration: Coordinates shared memory between agents.

705. How do you handle rollback or cancellation in multi-step GenAI agents?

  • Rollback strategy:

    • Maintain checkpoints per step (e.g., via Redis or DB).

    • Compensating actions (e.g., undo DB writes or file saves).

  • Cancellation:

    • Token-based cancellation via APIs or orchestration tools.

    • Early exit conditions using control nodes (e.g., user abort or timeout).

  • Tools: Prefect (with cancel API), LangGraph with error states.

706. How can you monitor execution traces across tools, prompts, and user sessions?

  • Trace IDs: Assign unique trace/session IDs for correlation.

  • Instrumentation:

    • LangChain: langsmith, tracing middleware.

    • FastAPI: Middleware (e.g., OpenTelemetry, Sentry).

    • Qdrant: Custom logging around embedding/search APIs.

  • Logging levels: Capture input/output, latency, and failures.

  • Dashboards: Grafana (Prometheus), LangSmith, or Honeycomb.

707. What are failure recovery patterns in orchestration of LLMs and APIs?

  • Retry with backoff: For transient API failures (e.g., OpenAI rate limits).

  • Fallback prompts/models: If one LLM fails, use a smaller model or template.

  • Circuit breakers: Prevent flooding an API after repeated failures.

  • Graceful degradation: Provide partial output or default responses.

  • Idempotent steps: Ensure reruns don't corrupt the state.

708. How does orchestration change when working with streaming vs. completion-based models?

Aspect
Streaming Models
Completion-Based Models

Latency handling

Partial output updates in real time

Wait for full response

State updates

Incremental, token-level

After completion

User experience

More responsive

Batch-style

Complexity

Higher (needs streaming callbacks)

Simpler logic

Tools

LangGraph async support, SSE

Standard REST/SDKs

709. How would you structure a microservices architecture for GenAI agents?

  • Service Layering:

    • Frontend: React/Next.js or mobile client.

    • Gateway: FastAPI or GraphQL Gateway (rate limiting, auth).

    • LLM Orchestration: LangGraph/AutoGen or custom agents.

    • Vector Search: Qdrant/Weaviate microservice.

    • Storage: Postgres, Redis, Blob Storage.

  • Observability: Tracing (OpenTelemetry), centralized logging (Loki/Grafana).

  • Asynchronous Messaging: RabbitMQ or Redis Streams for long-running tasks.

  • Scalability: Kubernetes or ECS for containerized deployment.

710. What tools support tracing across LangChain, FastAPI, and Qdrant in a full-stack GenAI app?

  • LangChain: LangSmith for prompt/tool traces.

  • FastAPI:

    • OpenTelemetry (export to Jaeger, Grafana Tempo).

    • Sentry for error monitoring.

  • Qdrant:

    • Custom logging or use API hooks.

    • Wrap client calls with trace IDs.

  • Unified Tracing:

    • OpenTelemetry SDKs across stack.

    • Zipkin/Jaeger + Prometheus for metrics.

    • Prefect or Temporal for orchestration traces.

711. What is model distillation, and how does it reduce inference latency?

Model distillation is a process where a smaller "student" model is trained to mimic the behavior of a larger "teacher" model.

  • Process:

    • The student learns from the soft logits of the teacher, not just the hard labels.

    • Encourages learning richer inter-class relationships.

  • Benefits:

    • Reduces parameter count, thus lowering memory usage and inference latency.

    • Improves speed without a proportional drop in accuracy.

    • Enables deployment on edge devices or resource-constrained environments.

712. How does quantization affect attention patterns and token alignment in transformers?

Quantization reduces precision (e.g., FP32 → INT8/INT4), which can:

  • Introduce noise in attention weights and softmax outputs.

  • Slightly misalign token scoring, especially in early or mid layers.

  • Affect token order preservation, particularly in beam search or top-k sampling.

Advanced quantization-aware training (QAT) or techniques like GPTQ can help preserve alignment fidelity despite lower precision.

713. What are best practices for distilling a code generation model for production?

  • Use teacher-student alignment: Ensure the student captures both syntax and semantics.

  • Fine-tune on diverse programming languages and problem types.

  • Evaluate with code-specific metrics like CodeBLEU, Pass@k.

  • Incorporate unit-test based loss if possible (reward correct code execution).

  • Use prompt augmentation and synthetic datasets to enrich student training.

  • Use intermediate feature distillation (not just logits).

714. How do LoRA and QLoRA compare in terms of performance and cost?

Feature
LoRA
QLoRA

Use Case

Fine-tuning large models efficiently

Fine-tuning quantized models (e.g., 4-bit)

Cost

Lower than full fine-tuning

Even lower due to reduced memory needs

Memory usage

Moderate (adapters in FP16/BF16)

Very low (quantized base + adapters)

Speed

Fast

Slower than LoRA due to quantized math

Accuracy

Comparable to full fine-tune

Slightly reduced if quantization noise is high

🔧 QLoRA is ideal for resource-constrained training (e.g., on a single GPU), while LoRA is generally faster and more stable for production.

715. What are common accuracy tradeoffs in INT4 vs INT8 quantized LLMs?

Metric
INT8
INT4

Model size

~4x smaller than FP32

~8x smaller

Latency

Moderate speedup

Higher speedup

Accuracy drop

~1–2% in typical use

2–5% or more, esp. on complex tasks

Stability

Stable

Unstable in deeper reasoning tasks

Use case

General deployment

Edge or offline scenarios

INT4 is best when extreme compression is needed. INT8 is a safer default for production.

716. How do you evaluate performance post-distillation (BLEU, BERTScore, etc.)?

  • BLEU: Measures n-gram overlap; useful for code or structured text.

  • BERTScore: Embedding-based; captures semantic similarity.

  • ROUGE: Useful for summarization tasks.

  • Code-specific:

    • CodeBLEU, Exact Match.

    • Pass@k (for multi-attempt code generation).

  • Latency & Memory: Also benchmark inference speed and memory footprint.

Use a combination of automatic metrics and task-specific functional tests.

717. What’s the role of PEFT in task-specific fine-tuning for small devices?

PEFT (Parameter-Efficient Fine-Tuning) allows modifying only a small subset of parameters:

  • Techniques: LoRA, IA3, Adapters, BitFit.

  • Key benefits:

    • Reduces compute and memory costs.

    • Enables on-device fine-tuning or personalization.

    • Works well with quantized or distilled models.

PEFT makes it feasible to deploy task-tuned LLMs on mobile, edge, or embedded hardware.

718. How can pruning and sparsity be applied to generative architectures?

  • Pruning:

    • Removes less important weights or neurons (e.g., magnitude pruning).

    • Often applied post-training, with optional fine-tuning.

  • Sparsity:

    • Introduces structured sparsity (e.g., 2:4) for optimized hardware.

  • In LLMs:

    • Focus on attention heads, FFN layers, and embedding matrices.

    • Must avoid pruning key attention pathways or positional encodings.

  • Libraries: Hugging Face, OpenVINO, and PyTorch sparsity tooling.

719. What are common pitfalls when using quantized models with retrieval-based systems?

  • Embedding mismatch: Quantization may distort vector embeddings, lowering recall.

  • Incompatibility: Some libraries (e.g., FAISS exact search) expect FP32.

  • Latency bottlenecks: Quantized LLMs may be fast, but retrieval pipelines (e.g., vector DB) may not match speed.

  • Chunk mismatch: Quantized models may hallucinate more with improperly chunked inputs.

Mitigations:

  • Keep retriever and LLM precision-aligned.

  • Use distilled retrievers or quantized vector DBs (e.g., Qdrant with quantized HNSW).

720. How would you chain multiple lightweight models to act like a heavier LLM?

Use a modular pipeline:

  1. Router model: Classifies intent or task.

  2. Specialized models: Small models fine-tuned for narrow tasks (e.g., summarization, translation).

  3. Coordinator: Manages flow between models (like an agent or FSM).

  4. Memory & Context Store: Shared vector DB or token buffer.

  5. Fallback to larger model: Only if confidence thresholds not met.

This is similar to the "Mixture of Experts" or Hierarchical Agent architecture — optimizing cost while maintaining quality.

721. How do you estimate cost-per-call in a GenAI API deployment at scale?

To estimate cost-per-call:

  1. Break down by unit:

    • Input tokens × cost per 1K tokens.

    • Output tokens × cost per 1K tokens.

  2. Formula:

  3. Include overhead (e.g., vector search, orchestration, storage, logging).

  4. Scale for volume: Multiply by daily/monthly query volume.

For example, 800 input + 200 output tokens on GPT-4 Turbo (~$0.01 per 1K input, $0.03 per 1K output) = ~$0.008.

722. What’s the break-even point for self-hosted LLMs vs. OpenAI pricing?

Break-even depends on:

  • Model (e.g., LLaMA 3, Mixtral) and quantization level.

  • Monthly inference volume (e.g., >10M tokens/day).

  • Infra costs: GPU rental (A100/H100), storage, networking.

  • Ops/maintenance overhead.

Rough comparison:

  • OpenAI GPT-4 Turbo: ~$0.01–$0.03 per 1K tokens.

  • Self-hosted (GPU): Break-even around $15K–$25K/month in token equivalent, if:

    • You run 24/7 inference on dedicated GPUs.

    • Have strong batching + quantization.

High-throughput + stable prompts favor self-hosting; dynamic workloads may still prefer API.

723. How do you model ROI for a GenAI feature embedded in a SaaS product?

Model includes:

  • Benefit side:

    • Uplift in conversions, retention, or engagement.

    • Time/cost saved for users.

    • New pricing tier unlocks (monetization uplift).

  • Cost side:

    • Monthly token cost per user.

    • Model inference + vector DB + infra + human oversight.

ROI = (Revenue lift – Added cost) / Added cost

Use A/B testing, feature flags, and customer LTV estimates to model ROI over time.

724. What pricing strategies work best for AI-enhanced product tiers?

Effective strategies:

  • Feature-based upsell: Lock GenAI behind Pro/Enterprise tiers.

  • Usage-based billing: Charge per request/token beyond free quota (like OpenAI’s API).

  • Value-based pricing: Tier by outcome (e.g., auto-generated reports, resumes).

  • Hybrid: Base plan + credits for GenAI usage.

  • Overage alerts or caps: Prevent runaway costs and improve transparency.

Keep UX clean by abstracting tokens but showing cost in meaningful user actions.

725. How do caching strategies impact monthly token costs?

Caching reduces repeated calls, especially for:

  • Static prompts (FAQs, summaries, greetings).

  • Partial prompts (e.g., prefix + reused history).

Impact:

  • Reduces token usage by 20–80% depending on use case.

  • Cache hit rate is key (use vector DB or Redis for semantic caching).

  • Apply TTL and invalidation policies for dynamic content.

Caching is the #1 strategy to reduce GenAI costs without affecting output quality.

726. What’s your approach to cost forecasting across multiple GenAI endpoints?

Steps:

  1. Baseline usage:

    • Calls per endpoint × average tokens per call.

  2. Multiply by model rates:

    • GPT-4 vs. GPT-3.5 vs. Claude vs. LLaMA-hosted.

  3. Include fixed infra:

    • Vector DB, hosting, monitoring, scaling costs.

  4. Forecast scenarios:

    • Conservative vs. growth (e.g., 5× users, 10× requests).

  5. Tooling:

    • Use Prometheus/Grafana, LangSmith, or custom token tracking scripts.

727. How would you track cost per feature request in a multi-LLM system?

Use per-request metering:

  • Tag each user action or API call with:

    • Feature name.

    • Model used.

    • Token input/output.

    • Vector DB query cost (if any).

  • Store in Postgres or BigQuery for daily/weekly analysis.

Example schema:

Allows you to answer: “What’s the monthly cost of AI-powered autocomplete?”

728. How do you measure ROI when GenAI replaces manual content generation?

Key comparisons:

Metric
Manual
GenAI

Time per task

e.g., 30 mins

~1–3 mins

Labor cost

Human hourly rate

API cost (e.g., $0.01–$0.05/task)

Quality

Evaluate via scoring/rating

Needs human-in-the-loop or QA

ROI = (Labor cost saved – GenAI cost) / GenAI cost

For repetitive content (e.g., SEO, product descriptions, translations), GenAI can yield 10×+ ROI.

729. What role does latency cost (in user wait time) play in pricing tiers?

  • Users may pay more for faster GenAI (e.g., GPT-4 Turbo vs. GPT-4).

  • In B2B: Wait time = productivity loss → impacts willingness to upgrade.

  • Premium tiers can offer:

    • Priority inference queue.

    • Faster models (e.g., GPT-3.5 vs. GPT-4).

    • Preprocessing optimizations (streaming output, async updates).

Latency is a UX tax—worth monetizing via better tier SLAs.

730. How would you design cost-aware prompt routing using multiple model backends?

Design pattern:

  1. Router layer:

    • Inspect prompt metadata (task type, user tier, urgency).

    • Choose model:

      • gpt-3.5 for casual use.

      • Claude for long context.

      • LLaMA for internal users.

      • GPT-4 for critical cases.

  2. Confidence + fallback logic:

    • If response score < threshold → reroute to higher-tier model.

  3. Telemetry:

    • Log cost per route, success rate, latency.

  4. Tools:

    • LangGraph, AutoGen, or custom Flask/FastAPI middleware.

731. How do you audit for demographic bias in summarization or translation models?

To audit demographic bias:

  • Benchmark with diverse inputs:

    • Use test sets with variations in gender, race, ethnicity, and culture.

    • Examples: WinoBias, StereoSet, or custom datasets.

  • Compare outputs:

    • Check tone, omission, or misrepresentation by demographic.

  • Metrics:

    • Representation bias, semantic drift, and BLEU/F1 divergence across subgroups.

  • Human-in-the-loop:

    • Diverse reviewers validate summaries/translations.

  • Tooling: Evaluate outputs using fairness libraries (e.g., Fairlearn, Responsibly, CheckList).

732. What are your escalation protocols for AI-generated harmful content?

Escalation includes:

  1. Detection:

    • Use filters for toxicity, hate speech, misinformation (e.g., Perspective API, Azure Content Moderator).

    • Log flagged generations.

  2. Immediate response:

    • Quarantine or redact harmful output.

    • Notify responsible ML/ops team.

  3. Escalation steps:

    • Tier 1: Auto-flagged → reviewed internally.

    • Tier 2: Human moderation → stakeholder review.

    • Tier 3: Legal/PR escalation → external disclosure if needed.

  4. Postmortem + retraining:

    • Review prompt/model behavior and fine-tune or blacklist patterns.

733. How do you align product and legal teams around responsible GenAI usage?

  • Cross-functional AI governance committee:

    • Includes product, legal, compliance, and engineering.

  • Shared framework:

    • Define acceptable use cases, redlines (e.g., no health or legal advice).

  • Policy embedding:

    • Legal reviews in model development lifecycle.

    • Product checkpoints (pre-launch fairness, post-launch monitoring).

  • Training & playbooks:

    • Scenario-based alignment on ethical and legal risk.

734. What’s the role of third-party model audits in enterprise AI governance?

Third-party audits provide:

  • Independent evaluation:

    • Bias detection, safety benchmarks, fairness reviews.

  • Compliance:

    • Aligns with regulations (e.g., EU AI Act, NIST RMF, ISO/IEC 42001).

  • Trust & transparency:

    • Build confidence for B2B/enterprise customers.

  • Typical outputs:

    • Audit reports, scorecards, model cards with bias/safety grades.

Vendors: Credo AI, AI Fairness 360, Partnership on AI auditors.

735. How do you apply fairness metrics to prompt-level evaluation?

  • Per-prompt fairness testing:

    • Run counterfactual prompts: Change demographics and compare outputs.

    • Example: "She is a nurse" → "He is a nurse" → assess semantic shift.

  • Metrics:

    • Jensen-Shannon divergence, Equalized odds, Group fairness.

  • Tools:

    • Custom scripts or fairness-focused prompt testing frameworks.

    • Use sliced accuracy or group BLEU in generation tasks.

736. What organizational safeguards are needed to prevent GenAI misuse?

  • Technical safeguards:

    • Prompt filters, content moderation, rate limiting, and audit logging.

  • Policy safeguards:

    • Internal GenAI usage policy.

    • Role-based access control to sensitive features.

  • Governance:

    • Risk review boards for new features.

  • Education:

    • Regular training on responsible AI and misuse scenarios.

  • Incident Response Plan:

    • Defined playbook for misuse detection and action.

737. How do you conduct bias and fairness reviews of prompts used by customer-facing agents?

  • Prompt library audits:

    • Review prompt templates for stereotypes, assumptions, or exclusion.

  • Simulated prompt testing:

    • Run prompts with diverse user inputs and personas.

  • Fairness checklist:

    • Inclusive language, neutrality in tone, and equal treatment of entities.

  • Red-teaming:

    • Intentionally stress-test prompts with adversarial or edge-case inputs.

738. What’s your view on disclosing AI-generated content to end-users — opt-in, opt-out, or visible by default?

Best practice: Visible by default, with clear indicators.

  • Promotes trust, accountability, and user consent.

  • Aligns with emerging regulations (EU AI Act: transparency requirement).

  • Add “AI-generated” labels or icons, especially for:

    • Summaries

    • Conversations

    • Generated images/content

Opt-out or hidden labeling risks backlash and ethical issues.

739. How would you handle a public GenAI output incident (e.g., offensive, misleading)?

  1. Immediate response:

    • Acknowledge the issue publicly.

    • Disable affected feature or route.

  2. Internal investigation:

    • Reproduce issue and analyze root cause (prompt, model, retriever).

  3. Corrective action:

    • Patch prompts, apply filters, retrain if needed.

  4. Postmortem:

    • Document, share findings internally, and update safety practices.

  5. External disclosure:

    • Depending on severity, publish a transparency note.

740. What are your practices for documenting known limitations of GenAI features?

  • Model cards:

    • Include limitations on domain knowledge, hallucination risk, bias potential.

  • In-product disclosures:

    • Tooltips, modals, or footnotes on “AI-generated content”.

  • Public documentation:

    • Describe what the GenAI system can/can’t do (e.g., “Not for medical advice”).

  • Ongoing updates:

    • Reflect feedback loops, new findings, or incidents.

Clear documentation is a critical pillar of AI governance and user trust.

741. How do you design interfaces that balance GenAI autonomy with user control?

Design for human-AI collaboration:

  • Editable prompts: Let users review or edit prompt before submission.

  • Undo/Redo options: Enable reversal of AI actions.

  • Multi-choice responses: Offer multiple AI-generated options (e.g., drafts, summaries).

  • Control levers: Add sliders/toggles for creativity vs. precision, tone, or length.

  • Manual override: Let users take over or adjust responses.

Balance automation with transparency to foster agency and trust.

742. What is progressive disclosure in GenAI UX and when should you use it?

Progressive disclosure shows basic output first, with deeper details revealed upon user action.

When to use:

  • Long documents (e.g., contracts, reports).

  • Layered insights (e.g., GenAI + data visualizations).

  • Avoid overwhelming new users with technical or verbose responses.

Examples:

  • “Show more”, “Reveal sources”, “Expand explanation”.

  • Tooltips for deeper model reasoning.

743. How do you present AI confidence or uncertainty in responses?

Approaches:

  • Visual cues:

    • Confidence bars, colored borders, or icons (e.g., ⚠️ for low confidence).

  • Inline disclaimers:

    • “AI may be inaccurate on this topic” or “Low reliability warning”.

  • Explainability overlays:

    • “Why this answer?” with source links or retrieved facts.

  • Temperature-based styling:

    • Stylize speculative outputs differently (e.g., italic, muted text).

Users appreciate knowing how sure the AI is — especially in critical domains.

744. What are best practices for revising, rerunning, or refining GenAI answers?

  • Inline editing + regenerate: Let users tweak input and rerun quickly.

  • Feedback controls:

    • “👍 / 👎”, “Refine this”, “Try again with different tone”.

  • Prompt history:

    • Show versioned iterations of user prompt + GenAI response.

  • Preset tweaks:

    • One-click options like “Make it shorter”, “Add stats”, “Rewrite formally”.

Treat refinement as an iterative dialogue, not a one-shot task.

745. How do you segment GenAI experiences for different user personas (e.g., novice vs. expert)?

  • Mode switching:

    • Simple vs. Advanced (e.g., toggles for “Quick Draft” vs. “Prompt Studio”).

  • Preset templates:

    • Use cases catered to skill level (e.g., “Write a tweet” vs. “Craft an SEO strategy”).

  • Guided vs. freeform:

    • Novices get walkthroughs; experts get raw prompt playgrounds.

  • Explain-as-you-go:

    • Tooltips and helper text for novices.

Personalization improves UX and reduces drop-offs across the user spectrum.

746. What’s your approach to onboarding users into a GenAI tool?

Effective onboarding includes:

  • Interactive walkthroughs:

    • Show prompt examples, retry flows, output edits.

  • Skeleton interface with examples:

    • Pre-filled outputs to set expectations.

  • Mini-missions or tasks:

    • “Try editing this prompt”, “Test generating a summary”.

  • Prompt library:

    • Curated, use-case-specific starters.

  • No-code guardrails:

    • Default settings that yield safe, useful outputs without needing prompt tuning.

747. How do you visualize long GenAI outputs in a scrollable or collapsible way?

Patterns:

  • Collapsible sections:

    • “Show summary”, “Expand details”, “Read full version”.

  • Anchored navigation:

    • TOC with jump links (e.g., headings: intro, benefits, risks).

  • Chunk-by-chunk reveal:

    • Stream output in paragraphs (esp. for chat).

  • Side-by-side comparison:

    • Use tabs to toggle between drafts/versions.

Prioritize scan-ability and user pacing to avoid fatigue.

748. What are the design tradeoffs between chat-based vs. form-based GenAI inputs?

Aspect
Chat-based
Form-based

Flexibility

High (natural language)

Structured (defined fields)

User control

Moderate

High

Learning curve

Lower

Higher (but safer for tasks)

Use cases

Brainstorming, writing, ideation

Data input, task automation, reporting

Error handling

Requires clarification cycles

Validations can be embedded

Hybrid UIs (form-backed chat) offer the best of both worlds.

749. How do you build trust in GenAI for critical tasks (e.g., finance, legal)?

  • Human-in-the-loop review:

    • Label outputs as “Draft only, not final”.

  • Explainability:

    • “Cited from XYZ” or “Based on retrieved policies”.

  • Disclaimers:

    • In UI and terms of service (e.g., “Not legal advice”).

  • Auditability:

    • Keep logs of GenAI decisions or suggestions.

  • Version locking:

    • Freeze model versions used for compliance-sensitive workflows.

750. How do you collect product telemetry to improve GenAI UX over time?

Track:

  • Prompt types + length.

  • Token usage per endpoint.

  • User interaction events:

    • Edits, reruns, likes/dislikes, aborts.

  • Drop-off points:

    • Where users leave or fail to proceed.

  • Success metrics:

    • Time to task completion, downstream conversions.

Tools:

  • PostHog, Mixpanel, Segment, LangSmith, or custom OpenTelemetry pipelines.

Close the loop with product + model teams for iterative improvement.

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