H2O Generative AI Starter Track

Master prompt engineering to maximize generative ai performance, guiding llm outputs with high-quality prompts, roleplay prompts, zero-shot prompting, guard rails, and prompt chaining for relevant, accurate responses.

Explore retrieval-augmented generation (rag) to deliver accurate, data-backed responses. Learn how retriever, generator, embedding model, and vector database enable retrieval and indexing for customer support, financial analysis, and knowledge management.

Explore generative AI essentials, from machine learning and deep learning to large language models, with prompt engineering, retrieval-augmented generation, and real-world applications.

Leverage h2oGPTe to transform your data into actionable insights through prompting, data ingestion, configuration, and RAG (retrieve and generate), plus API access for programmatic integration.

Explore h2oGPTe’s generative ai platform, leveraging retrieval-augmented generation to analyze documents and deliver context-aware answers. Learn prompting, data ingestion, configuration, RAG workflows, and API access, including air-gapped setups.

Use generative AI on your documents with h2oGPTe to generate a first draft from uploaded articles, draft a speech, and build self-service HR or procurement chatbots, boosting efficiency.

Explore the free h2oGPTe platform to connect any LLM or embedding model, with guardrails, retrieval-augmented generation, built-in citations, and enterprise deployment options.

Master simple ai interactions with the h2ogpte chat by following five steps: prompting, collection, configuration, rag, and api to extract information from documents and get clear, unbiased answers.

Create a document collection with h2oGPTe to enable retrieval-augmented generation by uploading diverse files, configuring embedding models and chunk sizes, and indexing them into a vector database.

Configure chats in h2oGPTe using three tabs: Documents tab, Configuration tab, Prompts tab to tailor LLMs, vision, RAG, and HyDE workflows; enable Use Agent and customize prompts for precise outputs.

Demonstrate context-aware responses with h2ogpte by querying factors of drug desistance, retrieving answers anchored to the uploaded document, and showing references with relevant pages and scores.

discover how to create and use api keys for h2ogpte, including global and collection-specific keys, and integrate with the python client libraries.

learn to design system prompts and dynamic user inputs to guide AI in producing structured reports, with steps to build a prompt template and ensure consistent, professional outputs.

Integrate multiple specialized models with a minimalist single-agent design to optimize tasks. Execute code-first actions through the CodeAct framework, enabling dynamic code execution and adaptive error handling in real time.

Review core GenAI skills: use prompt templates for precise AI responses, manage data in collections across 65 file types, harness retrieval-augmented generation, and tailor settings and APIs for apps.