Generative AI Essentials 2 Expertise: Complete Concise Guide

Generative AI uses large language models to generate text, images, videos, and music from prompts, relies on transformer architecture, and remains stateless unless applications provide memory.

Explore the current state of generative ai, from OpenAI and Google to open-source models, and discover how Jenai powers text, images, video, music, code, and more today.

Master token-based pricing and practical usage for gen ai, comparing GPT-4 and GPT-3 costs, exploring free tiers, and emphasizing cost control.

Discover a typical generative AI architecture, from the large language model brain and application layer to the front end, using session memory and vector databases for retrieval augmented generation.

Explore essential gen ai terms and architectures, from ai and ml to llms and transformers, and master prompts, hallucination, temperature, fine-tuning, tokens, and embeddings.

Craft clear, specific prompts to guide large language models using context and method as core elements. Iterate to refine outputs and understand how prompts influence responses.

Master practical prompt engineering techniques for interacting with large language models. Include context, step-by-step instructions, few-shot and template prompts, persona prompts, retrieval augmented generation, safety and ethics.

Master advanced prompting techniques: chain of thought, tree of thought, and self-reflection, to enable multi-step reasoning, plus multimodal prompting for images, videos, and insights.

Continue advancing prompt engineering by self-learning from internet sources, using the base prompt, refining with technique hints, and leveraging creativity to craft the best prompts.

Explore practical prompt engineering techniques using ChatGPT and Gemini, including zero-shot, one-shot, few-shot prompts, templates, chaining, and blogger prompts, with hands-on examples.

Master advanced prompting techniques, including chain of thought and tree of thought, with multimodal prompts using Gemini and ChatGPT, and generate test data for quick test cases.

Learn how retrieval augmented generation leverages external sources—databases, documents, or the web—to deliver accurate, customized responses. Apply the retrieve, augment, and generate pattern for real-time, domain-specific prompts.

Understand the Rag pattern with retrieval, augmentation, and large language model generation, driven by an application layer that uses sql and similarity search to tailor prompts.

Apply similarity search and embeddings to the retrieval augmented generation (rag) pattern, enabling semantic data retrieval across text, images, and audio via vector representations, with retrieve-augment-generate steps.

Access large language models programmatically by writing python code in colab, using hugging face tokens and gpt-2, and explore prompt engineering with code examples across python, java, and node.js.

Demonstrate a rag implementation using SQL search in Colab, retrieving from a knowledge-base xls with a Gemini API key, and generating step-by-step instructions from an LLM.

Contrast similarity search with sql search in retrieval, semantically matching prompts to knowledge base issues using embeddings and cosine similarity with Gemini's model to retrieve and augment solutions.

Learn how fine tuning refines a pre-trained model by training on domain-specific data to boost accuracy, relevance, and style, using hundreds to thousands of examples alongside prompt engineering and rag.

Fine-tuning hinges on high-quality labeled data and guarding against overfitting with early stopping and dropout, while leveraging scalable infrastructure and parameter-efficient methods, plus bias testing for fairness.

Explore fine tuning techniques for large language models, including full model training, low rank adaptation, and adapter layers, using data from GPT-4 to tune GPT-3.5 cost-effectively.

Fine-tune a generic language model with a custom question-and-answer dataset in Colab using the Gemini 1.5 base model, then test and tailor a domain-specific chatbot.

Assess key security risks in gen ai systems, including data breaches, adversarial attacks, model theft, and misuse, then implement encryption, access controls, and audits to build trustworthy, transparent systems.

Embed ethics into AI development to prevent bias and unaccountable decisions. Apply fairness checks, privacy safeguards, and transparent governance to build trust.

Explore why explainable AI matters for trust, debugging, fairness, and accountability in high-stakes domains, and learn techniques like attention visualization and counterfactual explanations.

Start with prompt engineering to add context and instructions, then use Rag to bring in up-to-date data, and finally fine-tune the model for style and domain nuances to boost performance.

Build an end-to-end JNI storyteller app with a three-layer architecture (front end, application layer, llm) using Google Cloud and Gemini JNI, powered by prompt engineering.