Python for AI: Master Prompt Engineering & LLM Development

Compare the right large language models for your ai app by weighing size, performance, cost, and safety, including GPT-4 Omni, Gemini, and Grok 3, with attention to transparency and pricing.

Explore prompt engineering for guiding LLM-powered apps, from fundamental methods to advanced techniques, to improve reliability and reduce hallucination and bias. Practice with an OpenAI account, API, and Python.

Explore how prompt engineering guides large language models to generate structured outputs from input prompts, enabling tasks like named entity recognition, sentiment analysis, and summarization.

Identify and deliver precise instructions to guide AI, defining the goal, format, constraints, and background. Learn to extract actionable steps from text and convert them into a bullet list.

Enhance llm reliability with justification-based prompting that supports error recovery, transparency, and user trust by requiring model justifications, generating multiple outputs, and using meta prompts to improve reasoning.

Explains recency bias in large language models and how token processing and attention shape output. Shows prompt-engineering tactics like repeating instructions at the end and breaking tasks into subtasks.

Master few-shot learning to guide ai behavior with minimal data using structured examples. Leverage GPT-4.0 for improved context and consistent prompt engineering across marketing copy and classification tasks.

Define and print variables in Python by exploring data types, the interpreter's role, and the print function, while observing syntax highlighting and dynamic value changes in program output.

Learn how to read Python tracebacks and diagnose common errors by checking variable names, misspellings, and undefined variables, then fix issues quickly.

Master the assignment operator and the distinct meanings of a equals b versus b equals a. See how values flow between variables and how assignments update A and B.

Learn how strings work as a data type and how to apply string methods in Python to change case, format text with title, upper, and lower, and handle user inputs.

Learn how f strings in Python insert variable values into strings using braces, formatting with a leading f, and printing a composed message like a car model and year.

Learn how to use whitespace, tabs, and newlines in Python strings to format output, including printing with tabs and line breaks and combining them for multi-line lists.

Master string manipulation in Python by trimming whitespace with strip, lstrip, and rstrip, and removing prefixes like https for reliable username checks and URL processing.

Learn how to handle Python syntax errors related to single and double quotes, string literals, and backslash escapes. Explore how proper quoting prevents interpreter errors and improves code reliability.

Learn how to slice lists in Python to select subsets, using start, end, and negative indices, and loop through slices with a for loop to print or process items.

Copy a list with a full slice to create a separate copy, then append different items to each list to keep them independent, as Toyota and Fiat illustrate.

Learn how keyword arguments let you pass function parameters by name, avoiding order mistakes, and how default values let you omit parameters with Python examples.

Explore how functions return values via a return statement, using a get car info function that takes maker, model, and year to format a full car description.

Explore object oriented programming by defining classes and creating instances. Use an init method to set attributes like make, model, year, and gearbox, and implement drive and stop actions.

Create an instance from a class by using an init method to set make, model year, and gearbox for a car, and learn to access and print its attributes.

Use dot notation to access class attributes and methods, creating multiple car instances like BMW and Honda with their make, model, year, gearbox, and actions such as drive and drift.

Learn how Python inheritance lets a child class inherit attributes and methods from a parent class, then extend it with new features, as shown with car and electric car.

Explore embedding large language models in applications with LangChain and Hugging Face models, and set up development with key dependencies and API access.

Harness LangChain to integrate language models, prompt templates, memory chains, and agents, enabling ai powered applications with multiple providers and both completion and chat models.

Explore data connections that bring external information into AI models with LangChain document loaders, text splitters, embeddings, and vector stores to enable retrieval from CSV and other sources.

Explore text splitting with a recursive character text splitter to create 100-character chunks with 20-character overlap. Learn about text embeddings and vector databases for storing and retrieving related content.

Learn how to create and store document and query embeddings with OpenAI Ada 002, and use a vector store and cosine similarity for fast retrieval in large language model apps.

Build an llm powered conversational travel assistant using Lang chain, memory, and non-parametric knowledge to fetch live data and create autonomous and agentic experiences.

Turn a plain vanilla bot into a context aware, memory enabled chat by integrating memory with a conversation chain using lang chain, enabling multi turn conversations.