Question 1

What is generative AI development?

Accepted Answer

Generative AI development is the process of building applications powered by large language models (LLMs) and diffusion models that can create new content — text, images, code, audio, and structured data. Unlike traditional software that follows fixed rules, generative AI produces original, context-aware output. We build production systems on top of models like GPT-4, Claude, Llama, Mistral, and Stable Diffusion.

Question 2

Which generative AI models do you work with?

Accepted Answer

We work with both commercial and open-source models: OpenAI (GPT-4o, GPT-4), Anthropic Claude, Google Gemini, Meta Llama 3, Mistral, and Stable Diffusion / FLUX for images. We help you choose between hosted APIs (faster to ship) and self-hosted open models (better for data privacy and cost at scale), then build the surrounding application.

Question 3

Can you build a custom generative AI solution using our own data (RAG)?

Accepted Answer

Yes. Retrieval-Augmented Generation (RAG) is one of our core offerings. We connect an LLM to your private knowledge base — documents, databases, support tickets, product catalogues — using a vector database (Pinecone, Weaviate, pgvector). The model then answers questions grounded in your actual data, dramatically reducing hallucinations and keeping responses accurate and current.

Question 4

How do you prevent hallucinations and ensure output quality?

Accepted Answer

We use a combination of RAG grounding, prompt engineering, structured output validation, guardrails (content filters and schema enforcement), and human-in-the-loop review for sensitive use cases. For high-stakes applications we add evaluation pipelines that score outputs against a test set before every deployment.

Question 5

How long does it take to build a generative AI application?

Accepted Answer

A focused proof-of-concept (e.g. a RAG chatbot over your docs) can be delivered in 2–4 weeks. A production-grade application with authentication, monitoring, evaluation, and scaling typically takes 8–16 weeks depending on complexity and integration requirements.

Question 6

Is my data safe when using generative AI?

Accepted Answer

Yes. For privacy-sensitive workloads we deploy open-source models in your own cloud (AWS/GCP/Azure) or via enterprise API tiers that contractually exclude your data from model training. We sign NDAs, implement data-access controls, and can build fully on-premise solutions where regulation requires it.

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