Frequently Asked Questions

Generative AI refers to models that create new content or data, such as text, images, audio, or code. These models learn patterns from existing data and produce novel outputs that are conditioned on prompts or context.

Generative AI accelerates content creation, automates document processing, enhances personalization in marketing and retail, and surfaces insights from unstructured data. Use cases include automated report drafting, knowledge base generation, and contextual summarization.

Large language models are a subclass of generative AI that specialize in producing and understanding natural language. Generative AI more broadly covers models that generate images, audio, code, or structured data as well as text.

High-value use cases include intelligent document processing, conversational AI and virtual assistants, content generation and personalization, knowledge intelligence and retrieval augmented generation, data analysis and summarization, and code generation for developer productivity.

Building agents involves selecting or training models, fine-tuning with domain data, assembling multi-agent orchestration when tasks require multiple capabilities, and grounding outputs using retrieval augmented generation to provide factual support. It also requires integration into production systems, robust testing, and continuous monitoring.

Generative AI can be safe when governed properly. Effective measures include bias mitigation, data encryption, role-based access controls, detailed audit trails, explainability strategies, and ongoing monitoring for drift or misuse. Combining technical safeguards with policy and human review reduces operational and compliance risk.

Accuracy for generative AI is measured with standard information retrieval and classification metrics such as precision, recall, and F1 score, as well as task-specific metrics like BLEU, ROUGE, or human-evaluated correctness depending on the use case. Accuracy depends on the quality and representativeness of training data, model architecture, and the evaluation protocols that are used. ATC improves model performance using supervised fine-tuning, reinforcement learning from human feedback, and controlled A/B testing against real user interactions. For production use, accuracy should be validated with held-out test sets, real-world pilots, and ongoing monitoring to detect drift.

Hallucination occurs when a generative model produces plausible sounding content that is factually incorrect, fabricated, or unsupported by available data. Hallucinations can appear as invented facts, incorrect citations, or confidently stated but false statements. ATC treats hallucination as a critical failure mode and mitigates it through grounding, validation, and human oversight.

To reduce hallucinations, combine retrieval-augmented generation to ground responses in authoritative documents, apply reinforcement learning from human feedback to shape preferred behaviors, run systematic bias and sanity checks during development, and include human-in-the-loop review for high-risk outputs. Additional measures include prompt engineering that forces source attribution, input sanitization, confidence scoring with fallback paths, and ongoing post-deployment monitoring that flags and retrains on hallucination examples.

Retrieval-augmented generation is a technique that improves response factuality by having the language model fetch relevant documents or data at query time and then generate answers conditioned on that retrieved content. RAG systems combine information retrieval methods, such as hybrid semantic and lexical search, with the generative model so outputs can be cited and verified. This approach reduces unsupported assertions and enables traceability to source material.