> ## Documentation Index
> Fetch the complete documentation index at: https://docs.encord.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Gen AI

> Build grounded, high-fidelity generative AI systems with curated data, human feedback, and continuous evaluation.

# Gen AI

Build **grounded, reliable, and production-ready generative AI systems** by treating data quality, human feedback, and evaluation as first-class concerns.

Frontier and generative AI systems are only as strong as the data that supports them. Encord helps teams curate, label, and evaluate **large-scale multimodal datasets** so models behave predictably—rather than producing untraceable or inconsistent outputs.

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## What you’re building

Gen AI systems increasingly power **core product experiences**, not experiments. These systems often support:

* Retrieval-Augmented Generation (RAG)
* Preference-based model tuning and RLHF
* Multimodal generative assistants
* Agentic and tool-using workflows
* Large-scale summarization and analysis

To operate reliably in production, these systems require:

* High-quality, curated source data
* Structured human feedback loops
* Continuous evaluation of model behavior
* Clear visibility into error modes and failure cases

***

## Key challenges in frontier and generative AI

Teams building frontier-scale Gen AI systems commonly face:

* **Unstructured and noisy data** spread across many sources
* **Hallucinations and grounding failures** that are hard to diagnose
* **Inconsistent or ad-hoc human feedback**
* **Limited observability** into how models behave over time

Solving these problems requires more than better prompts—it requires **intentional data workflows**.

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## How Encord supports Gen AI

### 1) Centralize and structure unstructured data

Ingest documents, text, images, audio, and metadata into a unified workspace so teams can explore, filter, and understand their data before using it for training or retrieval.

### 2) Curate data for signal, not volume

Identify low-quality sources, duplicates, hallucination triggers, and edge cases using filtering, embeddings, and targeted collections.

### 3) Collect high-quality human feedback

Design structured annotation workflows for classification, ranking, preference selection, and safety evaluation—so feedback is consistent, reviewable, and measurable.

### 4) Evaluate model behavior continuously

Compare outputs across prompts, datasets, and model versions to surface regressions, bias, and alignment gaps early.

### 5) Close the loop with iteration

Feed evaluation insights back into curation, annotation, and retrieval strategies to continuously improve model behavior over time.

***

## Common Gen AI workflows

### Retrieval-Augmented Generation (RAG)

Improve accuracy and grounding by pairing LLMs with curated, trusted knowledge sources and measurable retrieval evaluation.

### RLHF and preference learning

Capture human judgments to align models with desired behavior, tone, correctness, and policy constraints.

### Multimodal Gen AI

Combine text, documents, images, and audio to support richer, more capable generative systems.

### Agentic systems

Coordinate multiple LLM calls, tools, and decision steps with structured evaluation and review.

***

## Recommended starting points in the docs

### Data ingestion & structuring

* [Files](/platform-documentation/Curate/index-files)
* [Custom Metadata](/platform-documentation/Curate/custom-metadata/index-metadata-schema)
* [Supported Data](/platform-documentation/General/general-supported-data)

### Data curation & filtering

* [Getting Started with Index](/platform-documentation/Curate/index-getting-started)
* [Embedding Plots](/platform-documentation/Curate/embedding-plots)
* [Collections](/platform-documentation/Curate/curation-basics)

### Annotation & human feedback

* [Get Started with Annotate](/platform-documentation/Annotate/annotate-gettingstarted/data-annotation)
* [Ontologies](/platform-documentation/Annotate/annotate-ontologies/annotate-ontologies)
* [Annotate & Review](/platform-documentation/Annotate/annotate-label-editor/annotate-label-editor-annotate)

### Evaluation & iteration

* [Quality Metrics](/platform-documentation/Validation/label-validation-basics#quality-metrics)
* [Model Evaluation](/platform-documentation/Validation/active-how-to/active-model-predictions-eval)

***

## What “good” looks like

You’re on track when:

* Generative outputs are **grounded and traceable**
* Feedback is **systematic, not anecdotal**
* Failure modes are **discoverable and repeatable**
* Improvements are **measurable across iterations**