Feedback Ingestion API

The primary entry point for all feedback signals. It enforces a structured payload schema that standardizes every incoming event. A robust schema includes:

• inference_request_id: A unique identifier to join feedback with the original model input and output logs.
• model_version: The specific model iteration that generated the output.
• feedback_type: Categorizes the signal (e.g., explicit_correction, implicit_click, preference_pair).
• feedback_value: The actual signal (e.g., {"corrected_text": "..."}, {"preferred_output_id": "A"}).
• user_context: Optional metadata like session ID or user tier for bias analysis.

This structure enables deterministic feedback attribution and seamless integration into downstream data pipelines.

A middleware layer that performs integrity checks and augments raw feedback. The Feedback Validation Service applies rules to filter invalid data, such as schema violations, spam, or signals from malicious users. Concurrently, Feedback Enrichment attaches valuable context to each event, such as:

• The original model logits or embedding for the chosen output.
• User demographic segments from an identity service.
• Feature attributions (e.g., SHAP values) highlighting which parts of the input influenced the output.

This process transforms a simple thumbs-up into a rich training example, dramatically increasing its utility for diagnosing model behavior and driving targeted improvements.

The component that handles the continuous flow of feedback data. Using frameworks like Apache Flink or Apache Kafka Streams, it performs Feedback Stream Processing to:

• Route events to different downstream consumers (e.g., real-time dashboards, training buffers).
• Perform Real-Time Feedback Aggregation, calculating rolling metrics like accuracy, user satisfaction score, or reward model score averages.
• Trigger immediate alerts or model rollbacks if aggregated metrics breach predefined thresholds.

This capability shifts the system from passive logging to active monitoring, enabling sub-second responses to performance degradation or emerging issues.

The foundational storage pattern that guarantees a complete, auditable history. Instead of overwriting a central feedback database, every validated event is appended as an immutable record to a log (e.g., Apache Kafka topic or specialized event store). Event Sourcing for Feedback provides:

• A single source of truth for all feedback-related state changes.
• The ability to reconstruct the exact dataset used to train any past model version.
• Robust feedback attribution and auditability for compliance and debugging.
• Natural support for multiple consumers, as each service can read the log from any point in time.

This log becomes the canonical spine of the Continuous Training (CT) Pipeline.

A routing system that integrates human judgment into the automated loop. For high-stakes decisions, ambiguous feedback, or active learning queries, this gateway intercepts events and sends them to a human labeling interface (e.g., Label Studio, Amazon SageMaker Ground Truth). It manages:

• The queuing and prioritization of tasks for human reviewers.
• The collection of adjudicated labels or corrections.
• The reinjection of this high-fidelity data back into the main feedback stream as a golden dataset.

This component is critical for maintaining feedback fidelity, bootstrapping reward models, and handling edge cases where automated signals are insufficient.

The connectors that close the loop by feeding curated feedback into model learning systems. This involves:

• Feedback-to-Dataset Compilation: A batch job that joins enriched feedback with the original inference context from logs, applying feedback sampling strategies to create balanced training datasets.
• Experience Replay Buffer Management: In reinforcement learning systems, this service manages the storage and sampling of past (state, action, reward) tuples.
• Model Update Trigger: Listens to aggregated metrics or drift detection alerts and programmatically initiates Incremental Learning Jobs or full retraining pipelines.

This component directly controls feedback loop latency, determining how quickly user corrections translate into improved model performance.

The systematic capture of model inputs, outputs, and internal states (like logits or embeddings) during live prediction requests. This creates a traceable record that is essential for feedback attribution, allowing a Feedback Ingestion API to correctly link a user's feedback to the exact model version and context that generated the original prediction.

• Primary Purpose: Creates an immutable audit trail for every prediction.
• Key Data: Request ID, timestamp, model version, full input, raw output, and contextual metadata.
• Architecture: Typically implemented as a sidecar service or middleware in the model serving layer.

A predefined, versioned data structure that standardizes the format of all events sent to a Feedback Ingestion API. It defines the contract between client applications and the ingestion service.

• Core Fields: Inference request ID, model version, feedback signal (e.g., rating: int, correction: string), and user/session context.
• Importance: Enforces data consistency, enables schema validation on ingestion, and simplifies downstream processing.
• Example: A JSON schema requiring { "inference_id": "uuid", "model_version": "v4.2", "feedback_type": "correction", "value": "new_york" }

A dedicated service or component that applies integrity checks to incoming feedback before it enters the learning pipeline. It works in tandem with the Feedback Ingestion API to ensure data quality.

• Common Validations: Schema compliance, authentication/authorization, business logic rules (e.g., is the inference_id valid?), and spam/heuristic filtering.
• Outcome: Routes valid feedback to storage and invalid or malicious payloads to a dead-letter queue for analysis.
• Benefit: Prevents poisoned or malformed data from corrupting the training dataset.

The downstream ETL (Extract, Transform, Load) pipeline that transforms raw, validated feedback events into a curated training dataset. The Feedback Ingestion API is the source system for this pipeline.

• Key Steps: Joining feedback events with their original inference context (from logs), applying feedback sampling strategies, deduplication, and formatting for model consumption (e.g., creating preference pairs).
• Output: A versioned, incremental dataset ready for model retraining or online learning.
• Automation: Often triggered on a schedule or by volume thresholds.

The total time delay between a user interaction with a model's output and the integration of that feedback into an updated production model. The Feedback Ingestion API is a critical component in minimizing this latency.

• Stages: 1) User provides feedback, 2) API ingestion & validation, 3) Stream processing/aggregation, 4) Model update job, 5) New model deployment.
• Spectrum: Ranges from near-real-time (seconds/minutes for online learning) to batch-oriented (hours/days for full retraining).
• System Design Impact: Low-latency loops require tight integration between the API, stream processors, and online learning servers.

The process of correctly linking a piece of feedback to the specific model version, hyperparameters, and exact input data that produced the evaluated output. This is the foundational requirement that makes a Feedback Ingestion API useful.

• Mechanism: Relies on a unique inference_id or request_id that is logged during inference and included in the feedback payload.
• Challenge: Complexity increases with model ensembles, multi-step agents, or stateful sessions.
• Consequence: Poor attribution leads to ineffective or harmful model updates, as the learning signal is applied to the wrong context.