[NEW] 2026:Mastering Generative AI-From LLMs to Applications

Explore augmentation, where a language model assists humans to fetch information and respond to customers, and automation, where trained models handle inquiries with optional human intervention.

Explore how scale drives performance in neural language models, showing that bigger data and larger models improve outcomes, while compute, time, and budget constrain training.

Explore petaflops per day as a measure of training power and apply the Chinchilla paper’s balance between model size and data size to achieve compute-optimal training with billions of tokens.

Explore how transformers overcome RNN limitations by preserving long-range context through self-attention, encoder-decoder architectures, and vector space representations, enabling translation and sentence completion.

Discover the generative ai life cycle, from task definition and model selection to pre-training, prompt engineering, rag, fine tuning, and rlhf, culminating in tools, evaluation, and deployment.

Learn how retrieval augmented generation acts as a librarian for LLMs, retrieving information from external sources like web documents and company document repositories to augment responses with sourced data.

Fine-tune pre-trained language models to tailor domain vocabularies and tasks, using prompting and retrieval-augmented methods, while weighing open versus closed source options and training costs.

Learn reinforcement learning from human feedback, where humans rank LLM outputs to reward good results and penalize bad ones, aligning the model with human preferences.

Explore tools for reasoning with language models by calling external calculators and functions for calculations. Build agents that perform web research, API requests, and live data retrieval.

Master prompt engineering by crafting detailed prompts with task descriptions, inputs, and output formats; iterate to elicit honest, harmless, and helpful responses using zero-shot and few-shot in-context learning.

Contrast llama two and llama two chat, highlighting a 40% larger pre-training corpus and doubled context length, with the chat version fine-tuned for dialogue across 7b, 13b, and 70b variants.

Access llama 2 models via hosted API, self-configured cloud, or self-hosted, and tailor inference with system rules and settings like max new token length, top k, top p, and temperature.

Set up llama two models for prompt engineering, including installing Hugging Face Hub and Lama cp Python, and practice building system–user–assistant prompts with token and tuning controls.

Explore how stateless LLMs treat every prompt as a fresh start, requiring explicit context and prompt engineering to simulate statefulness and maintain conversation history.

Compare base LLMs and instruction tuned LLMs: base are generalists trained on broad data for diverse tasks, while instruction tuned models specialize on focused data to follow specific instructions.

Explore how system prompts, text prepended to prompts, guide model behavior and tone, enabling prompt templates for roles like a code generator and json output.

Quantized models shrink fp32 parameters to bf16, fp16, or int8 to save memory and speed up inference, with GML, GF, GPT-Q, and AWQ offering varied deployment and accuracy trade-offs.

Explore quantization methods for large language models, including GPT queue and activation-aware weight quantization, and run Zephyr 7B in Colab with direct preference optimization for human-aligned outputs.

Set up a quantized wq model with cipher 7 billion beta and half precision. Use a notebook containing the prompt template and table of contents for prompts.

Learn to check assumptions and conditions when using large language models, not for information retrieval, mitigate biases with factually accurate prompts, and verify task feasibility to ensure tasks are possible.

Learn prompt engineering by writing clear and specific instructions to reduce ambiguity, and use delimiters and system prompts to maintain context and control token length in LLM interactions.

Specify the output format to guide generative models toward structured results, such as json. The example produces a json list with book_id, title, author, and genre for three items.

Master few-shot prompting, from zero-shot to one-shot and few-shot, using 3–5 examples to train the model for a consistent style and patterned dialogue.

Break complex tasks into smaller steps and specify the required process in prompts. Encourage the model to work out its own solution before concluding and define the exact output format.

Prevent hallucinations by validating facts when asked about specifics. Prompt the model to use verified fact sheets and quote uncertainty when unknown, ensuring accurate product descriptions.

Develop prompts iteratively, starting with ideation and quick drafts. Test results, refine tone, vocabulary, and jargon, then repeat for better prompts and templates.

Identify common summarization issues, from overly long or short outputs to misfocused content, and learn to control length, domain focus, and output formats such as table, json, or html.

Learn to summarize text by setting a word limit and a focus, or extract specific details. Use examples like product reviews to practice focusing on shipping, delivery, and price.

Learn how to perform inference with language models and boost it through prompt engineering, enabling sentiment analysis, emotion detection, translation, tone customization, and handling multiple tasks at once.

Explore how prompt engineering enables transformation tasks with llms, including translation, tone transformation, format conversion, and grammar and spell checks.

Learn how to expand topics with prompts, tailor responses through role prompting and output formats, and use in-context learning with zero prompt short prompting and few short prompting.

Explore prompt tuning with soft prompts to adapt a frozen LLM to specific tasks, guiding outputs without full fine-tuning, and compare its cost, accuracy, and tradeoffs to multitask fine-tuning.

Discover how LangChain enables you to access multiple models—from OpenAI and Hugging Face to llama—by building chains, using agents and retrieval strategies, with templates and deployment tools.

Learn how to use ollama with LangChain to perform retrieval augmented generation on PDFs, including loading with unstructured pdf loader, recursive text splitter, gnomic embeddings, and Chroma DB.

Learn retrieval-augmented generation on CSV data by loading CSVs, creating embeddings, and querying a vector store with a Mistral model using LangChain and transformers.

Explore glue and super glue as benchmarks for evaluating large language models, detailing nine data sets and tasks like semantic textual similarity, inference, Q&A, and sentiment.

Fine-tune a Google plan T5 base model for dialogue summarization, train with tokenized data, and evaluate using Rouge metrics to compare baseline and instruct models.

Explore parameter efficient fine tuning (PEFT) and how freezing most of a language model with low rank adaptation through A and B adapters enables efficient multi-task learning.

Qlora quantizes LoRA for faster 8-bit training on a regular computer, using smaller layers and fewer parameters; it offers lower cost with a slight accuracy trade-off for limited resources.

Learn to implement PEFT with LoRA and Laura for parameter-efficient fine-tuning by training adapters while freezing the base model, including rank and target module configuration, and compare results.