From Recipe to Chef: Become an LLM Engineer 100+ Projects

Examine how training data and tokenization shape llms, with sources like Wikipedia, books, and web text. Emphasize that garbage in, garbage out and data bias affect model quality and fairness.

Explore training data as the pantry behind language models, highlighting diverse, high-quality sources—books, articles, dialogues, and code—and why cleaning and tokenization matter for bias-free, versatile AI.

Explore how tokenization splits text into tokens, examine word, subword, and character tokenization, and compare token limits and costs across models with practical analogies and examples.

Explore how llm datasets act as a grocery haul, using sources like Wikipedia, books, Common Crawl, OpenWebText, and Project Gutenberg, with cleaning and tokenization to craft diverse, high-quality training.

Garbage in, garbage out; learn how data quality shapes LLM performance, how cleaning data—removing duplicates, fixing text, and balancing sources—reduces bias and improves fairness.

Analyze how data bias shapes LLM responses across culture, gender, language, and topics, and learn strategies to reduce harm through diverse data, human feedback, and safety measures.

Train large language models at scale by iterating data through epochs and batches, using loss to guide improvement, and compare pre-training with fine-tuning while noting GPUs, TPUs, and training costs.

See how large language models learn language patterns through a training loop of mixing, baking, testing, and adjusting with data, tokens, and billions of parameters.

Explore how epochs, batches, and loss shape the training loop, with gradient descent adjusting the recipe after each batch to improve predictions and manage memory.

Discover how GPUs and TPUs act as industrial ovens for AI training, powering large models like GPT-3 and GPT-4, and how to access these resources via Colab or cloud providers.

Analyze the cost drivers of training large language models, including data, compute, energy, and expert engineers, and compare from-scratch training with fine-tuning and API-based approaches that save money.

Learn prompt engineering by likening prompts to spices, balancing context, instructions, and formatting to shape model output. Compare zero-shot, few-shot, and chain-of-thought prompts, and explore role-play, optimization, and evaluation.

Master prompt anatomy and flavor techniques to guide a language model, using clear instructions, context, input data, examples, output format, and constraints to craft precise, balanced prompts.

Learn zero-shot, few-shot, and chain-of-thought prompt styles with cooking analogies: salt, chili, herbs—and apply direct, example-based, and step-by-step reasoning, including role-play prompts.

Learn role play prompts for models, using persona, context, and tone with a simple three-ingredient recipe (role, task, style) and barista prompts to boost creativity and engagement.

Learn to transform raw prompts into well cooked instructions by refining task definitions, controlling format, tone, and length limits, reducing ambiguity, and iterating to produce clear, high quality LLM outputs.

Playful prompt evaluation as a taste test for prompts, assessing output quality and alignment with intent. Tune prompts to improve relevance, clarity, tone, format, and accuracy using the paste framework.

Fine tuning retrains a pre-trained model on a data set to tailor tone, domain knowledge, and behavior, using low rank adaptation and tools like Hugging Face, Google Colab, and Peft.

Fine tuning customizes a pre-trained base model with your data and tone, making it smarter, more reliable, and aligned with your brand beyond prompts.

Master transfer learning by using a pre-trained model and customizing it with fine tuning, prompt tuning, or domain adaptation to solve a new task with less data.

Compare full fine-tuning and LoRA (low-rank adaptation) for customizing base models, highlighting compute costs, data needs, speed, and modular adapter tuning.

Fine tune your ai chef on your own data to match your brand voice and jargon. Prepare labeled instruction or Q&A formats for fine tuning using Hugging Face, OpenAI, Colab.

Explore how to fine-tune large language models using Hugging Face transformers, Google Colab, and PEFT, loading a base model, training adapters, and deploying with Gradio.

Evaluate lms before serving them to users using both quantitative metrics and qualitative feedback. Assess perplexity, blue, and rogue; spot hallucinations and bias; refine prompts before launch.

Evaluate language models to identify errors, hallucinations, bias, and unsafe outputs, ensuring accuracy, safety, ethics, and reliability before and after training, fine-tuning, and deployment.

Learn to apply quantitative metrics—perplexity, BLEU, and ROUGE—to evaluate LLM outputs. Compare models using accuracy, fluency, and reference similarity, and balance these with qualitative metrics.

Assess qualitative metrics like usefulness, relevance, correctness, fluency, and tone through human feedback and rubrics to guide open-ended tasks, user feedback, and safety testing.

Detect bias in AI models through demographic swaps and benchmark datasets, then mitigate with inclusive data curation, prompts, and RLHF to respect diverse dietary and cultural needs.

Deploy LLMs by turning experiments into accessible APIs with FastAPI or Flask. Showcase with Gradio or Streamlit and host on Hugging Face or AWS, monitoring performance.

Deploy your trained llm by packaging it into an api or ui, hosting on cloud or edge, and connecting with a frontend like gradio or streamlit for users.

Build ai apis with fast api or flask by wrapping a language model behind a clean api endpoint that accepts prompts and returns generated text.

Learn to build quick, interactive demo UIs for language models using Gradio and Streamlit, turning a food truck window into a sleek UI for ML demos and portfolios.

Host your LLM app, including model, API, and UI, on Hugging Face Spaces, AWS, or GCP with public endpoints, uptime, and scalable CPU/GPU support.

Scale AI apps with vertical and horizontal growth, load balancing, and auto scaling, and monitor latency, throughput, and errors with Prometheus, Grafana, and cloud monitoring.

Learn to build LLM-powered apps with no-code and low-code tools, crafting chatbots, summarizers, and personal assistants; integrate SQL, NoSQL, and vector databases using AI chaining to deliver a capstone deployment.

Explore chatbots, summarizers, and recommenders as core app use cases on a no-code stack, using memory, rag, embeddings, and vector databases to tailor ai experiences.

Explore no-code tools for building LLM-powered apps with drag-and-drop interfaces, including long chain templates, GPT builder, and Voiceflow, to deploy chatbots and AI tools without coding.

Combine a language model with a database to build a smart menu that remembers orders and preferences for long-term memory, enabling data-driven personalization with relational, NoSQL, or vector databases.

Explore how LangChain enables multi-step AI pipelines with long chain memory, tools, and memory integration, using chains, retrievers, and agents to build modular, scalable AI apps.

Design, build, and deploy a fully functional lm powered app with a custom interface, no/low/full code options, and tools like memory, vector databases, and data integration.

Keep your llm apps fresh by using feedback loops, logging, and monitoring to refine prompts, while A/B testing and model drift tracking guide updates to datasets and deployments.

Explore how feedback loops transform real-world usage into improvements for artificial intelligence by collecting explicit, implicit, and corrective feedback, then logging signals to refine prompts and models for better alignment.

Log and monitor your AI app in real time to track user input, LLM outputs, latency, and health, with alerts for errors and slow responses.

Master A/B testing for language models by comparing two or more prompt and output variations, splitting users, and using metrics like clarity, latency, and engagement to choose a winner.

Learn how model drift changes performance over time, covering concept drift, data drift, upstream drift, and behavior drift, and apply monitoring, retuning prompts, data updates, retraining, version control, and rag.

Update prompts, datasets, and deployments to keep your LM app fresh. Follow a monthly freshness checklist with versioning, testing, re-embedding, and user feedback.

Explore career paths in llm engineering—from engineers to architects and prompt specialists. Build an ai cookbook portfolio, contribute to open source, and prepare resumes for a capstone llm app.

Explore three core LM career paths—engineer, architect, and prompt specialist—and learn how to grow through hands-on projects, from building and deploying models to designing prompts and system architectures.

Build your AI portfolio as a cookbook, showcasing projects, prompts, chains, and demos to prove your skills. Host 2–3 projects with readmes on GitHub or Hugging Face to stand out.

Learn to contribute to open source across datasets, models, prompts, tools, and docs, building reputation, collaboration, and real-world skills in the LLM engineering community.

Showcase LLM projects with tools, backends, data sources, and prompt engineering to nail resumes and interviews, including embeddings, vector databases, Rag, and long chain prompts.

Design, build, and deploy a full-stack LM powered app in this final capstone, delivering an end-to-end solution with UI, memory, and deployment to strengthen your portfolio for jobs.