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.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
- 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
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
Data curation & filtering
Annotation & human feedback
Evaluation & iteration
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