Generative AI Architectures with LLM, Prompt, RAG, Vector DB

Trace the evolution of artificial intelligence from rule-based systems to machine learning, deep learning, and generative artificial intelligence, and explore transformers and generative adversarial networks enabling creative content and applications.

Explore how generative AI creates new content by learning data patterns, differs from traditional AI, and enables scalable, augmented creativity across marketing, music, and software development.

Uncover how generative AI learns from datasets using training data and learning algorithms, with neural networks that detect patterns, then generates new content via output generation and a feedback loop.

Explore the three generative AI model types—GANs, VAEs, and transformer models like GPT—covering generator and discriminator dynamics, latent space reconstruction, and self-attention for text generation.

Explore the transformer architecture and self-attention, including encoder–decoder and decoder-only models like GPT, powered by multi-head attention for scalable language understanding and generation.

Explore how large language models work, from tokenization and billions to trillions of parameters, to real-world use cases like chatbots, translation, and content creation, while noting limitations and resource trade-offs.

Explore how transformer architectures with self-attention power large language models by processing text in parallel, tokenizing input, understanding context, predicting the next word, and scaling with bigger models.

Identify tokens as the building blocks llms use to process text, from words and punctuation to subword pieces, and see tokenization before processing with OpenAI's tokenizer.

Learn how LLMs use tokens, how tokenization breaks text into tokens, and how models predict the next token within input-output limits, with examples and OpenAI playground usage.

Explore the capabilities of LLMs, including text generation, summarization, translation, Q&A, chat comprehension, sentiment analysis, embeddings for semantic search, and code generation.

Examine the key limitations of large language models, including hallucinations and factual inaccuracies. Analyze how real-time knowledge gaps, limited context, and high computational costs affect reliability.

Explore how ChatGPT generates text and the capabilities and limitations of LLMs, including text generation, summarization, code generation, and question answering, with emphasis on factual accuracy and hallucinations.

Learn how temperature, max tokens, stop sequences, top p, and frequency and presence penalties shape LLM outputs, using OpenAI and Hugging Face playgrounds to explore creativity, detail, and deterministic outputs.

Learn how function calling lets large language models trigger external APIs to retrieve live data, book meetings, or perform calculations, transforming text generation into real-world actions.

Learn how large language models produce structured output in formats like JSON, XML, or key-value pairs for machine readability and seamless API and automation workflows.

Small language models offer faster, cheaper, and more efficient alternatives to large language models, enabling real-time edge computing, mobile apps, offline personalization, and lightweight text generation, classification, and language understanding.

Compare major large language model providers such as OpenAI, Meta AI, Anthropic, Hugging Face, Microsoft, and Google, and learn how they offer GPT, Llama, Palm, and Bert via cloud APIs.

Compare leading large language models, including OpenAI ChatGPT (GPT four), Meta Llama, Google Gemini, Anthropic Cloud, Mistral, and Grok, focusing on multimodal capabilities, context windows, and open source.

Explore small language models like GPT-4 mini, llama 3.2 mini, gemma, and phi-3, focusing on parameters, context windows, and efficiency for customer support and real-time applications.

Learn how to choose large language models by evaluating quality, speed, price, latency, and context window using the Artificial Analysis platform to make data-driven decisions for your applications.

Compare open source models like Meta's Llama, Bert, and GPT neo with proprietary models such as GPT four and Claude. Learn about accessibility, control, performance, and cost.

Explore Hugging Face, the GitHub of machine learning models. Explore open source models, datasets, and spaces to discover, share, and deploy AI across NLP, computer vision, and audio tasks.

Compare no-code chat UIs and with-code API key integrations to use large language models, via OpenAI GPT-4 API or Hugging Face, for apps like customer support chatbots and text summarization.

Compare responses from ChatGPT, Llama, Mistral, and Phi3 using chat interfaces to assess latency, context window impact, and response depth, guiding model selection for specific needs.

Learn to interact with OpenAI's chat completion endpoint to build chat-based apps and handle conversations. See Python and dotnet examples, enable streaming, and use GPT-4 mini for summarization and classification.

Olema enables running large and small language models locally, delivering offline privacy and low latency. Access pre-built models for coding, chat, and creative tasks with fine-tuning for domain-specific needs.

Install Ollama on your local computer and load Llama and Gemma models, run and manage models with commands, select parameter sizes, and enjoy fast, private on-device inference.

Learn how to modernize enterprise apps by integrating AI powered LLM capabilities through APIs and SDKs, enabling summarization, classification, semantic search, and Q&A with retrieval augmented generation.

Design an AI-powered eShop support app that uses LLM capabilities for ticket classification, summarization, semantic search, and Q&A with RAG citations to speed resolution and improve customer satisfaction.

Explore the four-step lms augmentation flow: prompt engineering, retrieval augmented generation, fine tuning, and a trained model, to tailor large language models for specific tasks.

Master prompt engineering, including zero shot, one shot, five shot, and chain of dots, to guide LLM responses and design clear, task-focused prompts for support applications.

Learn the four core prompt elements—instruction, context, input data, and output format—and how they shape model responses. Understand system and user roles to guide task execution and interaction.

Explore prompt engineering to craft prompts, experiment with zero-shot, one-shot, and five-shot prompting, and iteratively refine wording, structure, and tone to maximize model output quality.

Learn five-step prompt engineering: start simple, experiment, iterate, evaluate, and templatize; apply few-shot prompts, constraints, and templates to produce precise, high-quality results.

Explore advanced prompting techniques to improve language model outputs, including zero shot and one shot prompting, chain of thought, role- and instruction-based prompts, and contextual prompting with retrieval augmented generation.

Explore zero-shot prompting for large language models, using no prior examples and relying on pretrained knowledge. Understand when it suits simple tasks and why five shot prompting can improve results.

Learn one-shot prompting to guide large language models by providing a single example, shaping response format, tone, and structure with a practical product description task.

Explore five-shot prompting, a few-shot technique that uses examples to guide the model toward desired structure and tone, improving accuracy and ensuring consistent outputs.

Learn chain-of-thought prompting to elicit step-by-step reasoning from language models, enabling transparent, multi-step problem solving and clear explanations of how conclusions are reached.

Explore instruction based prompting and role based prompting to elicit precise, structured outputs, and use contextual prompting to guide responses within a specific domain.

Design advanced prompts for an eshop support app, enabling classification, sentiment analysis, summarization, and ai-powered q&a with retrieval augmented generation across ticket list and detail pages.

Design advanced prompts for the E-shop ticket detail page, enabling a Q&A chat with retrieval augmented generation and citations from product manuals, plus a suggested customer response generator.

Test advanced prompts for e-shop support using the OpenAI playground to classify tickets, analyze sentiment, summarize issues, and generate response text. Observe real-world scenarios and refine prompts across two pages.

Develop chat text completions and function calling with .NET, and create a .NET AI project solution using GPT five mini, including text completion, classification, sentiment analysis, and data extraction.

Learn to test and experiment with AI models directly within GitHub using GitHub modules. Explore the marketplace catalog, switch models, and prototype with the free tier and rate-limited access.

Create a personal access token for GitHub models to enable secure, read-only access in .NET applications, configure fine-grained permissions, generate the token, and store it safely for integration.

Build a dotnet console app that connects to GitHub OpenAI GPT-5 mini (GPT-4 mini) with Microsoft.Extensions AI. Configure secrets and endpoints to perform text completion, summarization, classification, and sentiment analysis.

Leverage the iChat client interface as a universal task engine to perform classification, summarization, and sentiment analysis through prompt engineering, switching code regions in a console application with OpenAI LMS.

Build an AI chat app in .NET using the GPT five mini model that remembers context by managing system, user, and assistant messages, with a stateful, streaming conversation.

Explore retrieval augmented generation (Rec) and its three components—retrieval, augmentation, and generation—to pull external knowledge for real-time, accurate, context-rich answers.

See how retrieval augmented generation lets LLMs access real-time data from external sources. Combine retrieval with generation to deliver accurate, context-specific answers from product manuals, research papers, APIs, and websites.

Explore retrieval augmented generation by ingestion, retrieval, and generation, and see data collected and indexed with embeddings in a knowledge base powered by vector databases to generate a response.

Explore the rag architecture, focusing on ingestion with embeddings and vector search to index documents and enable semantic, similarity-based retrieval.

Explore the RAG architecture retrieval flow from vectorizing queries with embeddings to retrieving relevant documents. Learn how ranking, filtering, and context query prompts create a context-rich prompt for generation.

Explore how the RAG architecture generates context-rich responses by synthesizing enriched prompts with pre-trained knowledge and knowledge-base details from documents, then delivers accurate, relevant outputs.

Navigate the end-to-end rag workflow from a user query through embeddings, vector retrieval, ranking, and context query prompt creation to generate a contextual, fact-based response with an llm.

Explore challenges of retrieval-augmented generation, including complex RAC architecture and external data integrations from APIs, databases, and document repositories. Examine latency, speed-accuracy trade-offs, infrastructure costs, and data quality.

Design an e-shop customer support system using retrieval augmented generation (rag), embedding-based vector stores, and context-aware responses from an llm to retrieve product manuals and FAQs.

Demonstrates end-to-end RAG for an e-shop customer support system, from uploading product manuals to generating context-aware responses via embeddings, retrieval, and LLMs in OpenAI Playground.

Build a dotnet ai chat web application using the microsoft extensions ai template, enabling retrieval augmented generation with a local on-disk vector store and GitHub models for custom data.

Explore a dotnet chat app with retrieval augmented generation, vector search, and embeddings, featuring a Blazor UI and PDF ingestion into a SQLite vector store.

Run and test a dotnet chat app with a retrieval augmented generation (RAG) workflow, ingest PDFs, create embeddings, and store vectors in a SQLite vector store with citation-driven answers.

Fine-tune pre-trained models to improve domain-specific performance by training on targeted data, with workflows, use cases, and methods illustrated through LM augmentation flow, including GPT-4 or Lama.

Fine-tuning embeds domain-specific knowledge and internal data into pre-trained models, enabling accurate, faster responses with shorter prompts and lower latency for specialized tasks.

Use fine tuning for domain-specific use cases requiring tailored knowledge; if not, prioritize prompt engineering or retrieval augmented generation to boost performance and reduce costs.

Fine tuning starts from a pre-trained language model and adapts it to a domain with a labeled dataset. It trains, evaluates, deploys, and maintains the model as base models evolve.

Explore fine-tuning methods for language models, from full fine-tuning to parameter-efficient techniques like LoRA and transfer learning, and learn when to apply each method based on data and resources.

Explore the challenges of fine-tuning, including overfitting and data quality. Learn strategies like regularization, validation, data augmentation, and resource-efficient methods (Pivot or Laura) to stay current with evolving models.

Fine-tune a base model to design an e-shop customer support system, covering data preparation, training, evaluation, and deployment to automate ticket responses and improve context aware accuracy.

End-to-end fine-tuning of a large language model for eshop customer support with the OpenAI playground, from json lines data prep to training and evaluation with humorous, structured outputs.

Compare prompt engineering, rag, and fine-tuning to optimize llm performance. Evaluate pros and cons, setup complexity, customization, latency, and maintenance to guide prototyping and production use.

Learn how to choose the right optimization for LLM powered applications using prompt engineering, RAG, and fine tuning, and how to combine them for accurate, real time, and efficient outcomes.

Explore training an LLM from scratch for optimization, including objective definition, data collection, prompt engineering and fine tuning, hyperparameter tuning, evaluation, and deployment, plus cost and privacy tradeoffs.