"Another problem that can be confusing is that LLMs seldom put out the same thing twice. [...] Traditional databases are straightforward - you ask for something specific, and you get back exactly what was stored. Search engines work similarly, finding existing information. LLMs work differently. They analyze massive amounts of text data to understand statistical patterns in language. The model processes information through multiple layers, each capturing different aspects - from simple word patterns to complex relationships between ideas." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)
"As the old saying goes, 'Garbage in, garbage out.' Generative AI tools are only as good as the data they’re trained on. They need high-quality, diverse, and extensive datasets to create great code as output. Unfortunately, you have no control over this input. You must trust the creators behind the product are using the best code possible for the corpus, or data used for training. Researching the tools lets you learn how each tool gathers data and decide based on that." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)
"Context is crucial for how language models understand and generate code. The model processes your input by analyzing relationships between different parts of the code and documentation to determine meaning and intent. [...] The model evaluates context by calculating mathematical relationships between elements in your input. However, it may miss important domain knowledge, coding standards, or architectural patterns that experienced developers understand implicitly." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)
"Context manipulation involves setting up an optimal environment within the prompt to help a model generate accurate and relevant responses. By controlling the context in which the model operates, users can influence the output’s quality, consistency, and specificity, especially in tasks requiring clarity and precision. Context manipulation involves priming the model with relevant information, presenting examples within the prompt, and utilizing system messages to maintain the desired behavior." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)
"Creating software is like building a house. The foundation is the first step; you can’t start without it. Building the rest of the house will be a struggle if the foundation doesn’t meet the requirements. If you don’t have the time to be thoughtful and do it right, you won’t have the time to fix it later." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)
"Design is key in software development, yet programmers often rush it. I’ve done this, too. Taking time to plan an app’s architecture leads to happy users and lower maintenance costs." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)
"First, training data is created by taking existing source code in many languages and feeding it into a model. This model is evaluated and has layers that look for specific things. One layer checks the type of syntax. Another checks for keywords and how they’re used. The final layer determines whether :this is most likely to be correct and functional source code'. There is a vast array of machine learning algorithms that use the model to run through these layers and draw conclusions. Then, the AI produces output that is a prediction of what the new software should look like. The tool says, 'based on what I know, this is the most statistically likely code you’re looking for'. Then you, the programmer, reach the evaluation point. If you give it a thumbs up, the feedback returns to the model (in many cases, not always) as a correct prediction. If you give it a thumbs down and reject it, that is also tracked. With this continuous feedback, the tool learns what good code should look like." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)
"Generative AI is a kind of statistical mimicry of the real world, where algorithms learn patterns and try to create things." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)
"Generative AI for coding and language tools is based on the LLM concept. A large language model is a type of neural network that processes and generates text in a humanlike way. It does this by being trained on a massive dataset of text, which allows it to learn human language patterns, as described previously. It lets LLMs translate, write, and answer questions with text. LLMs can contain natural language, source code, and more." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)
"Generative AI tools for coding are sometimes inaccurate. They can produce results that look good but are wrong. This is common with LLMs. They can write code or chat like a person. And sometimes, they share information that’s just plain wrong. Not just a bit off, but totally backwards or nonsense. And they say it so confidently! We call this 'hallucinating', which is a funny term, but it makes sense." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)
"Great planning and initial setup are crucial for a successful project. Having an idea and immediately cracking open an IDE is rarely a good approach. Many developers find the planning process boring and tiresome. Generative AI tools make these tasks more efficient, accurate, and enjoyable. If you don’t like planning and setup, they can make the process smoother and faster. If you enjoy planning, you may find these tools make it even more fun." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)
"In machine learning, 'training' is when we teach models to understand language and code by analyzing massive amounts of data. During training, the model learns statistical patterns - how often certain words appear together, what code structures are common, andhow different parts of text relate to each other. The quality of training data directly affects how well the model performs." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)
"It’s a pattern-matching predictor, not a knowledge retriever. It’s great at what it does, but since it works by prediction, it can predict nonsense just as confidently as it predicts facts. So, when you use these tools, be curious and skeptical! Don’t just accept what it gives you. Ask, 'Is this just a likely sounding pattern, or is it actually right?' Understanding how generative AI works helps you know when to trust it and when to double-check. Keeping this skepticism in mind is crucial when working with these tools to produce code." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)
"It’s essentially a sophisticated prediction system. Instead of looking up stored answers, an LLM calculates probabilities to determine what text should come next. While these predictions are often accurate, they’re still predictions - which is why it’s crucial to verify any code or factual claims the model generates. This probabilistic nature makes LLMs powerful tools for generating text and code but also means they can make mistakes, even when seeming very confident. Understanding this helps set realistic expectations about what these tools can and cannot do reliably." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)
"Professional software developers must know how to use AI tools strategically. This involves mastering advanced prompting techniques and working with AI across various files and modules. We must also learn how to manage context wisely. This is a new concept for most, and it is vitally important with code generation. AI-generated code requires the same scrutiny and quality checks as any code written by humans." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)
"Recursive prompting is a systematic approach to achieving higher-quality outputs through iterative refinement. Rather than accepting the first response, it uses a step-by-step process of evaluation and improvement, making it particularly valuable for complex tasks such as code development, writing, and problem-solving. Our example demonstrated how a basic factorial function evolved from a simple implementation to a robust, optimized solution through multiple iterations of targeted refinements." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)
"Stubbing is a fundamental technique in software development where simplified placeholder versions of code components are created before implementing the full functionality. It is like building the frame of a house before adding the walls, plumbing, and electrical systems. The stubs provide a way to test the overall structure and flow of an application early on, without getting bogged down in the details of individual components." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)
"Testing is like an investment. You spend time building tests now to strengthen your product. This approach saves time and frustration by catching problems early. As your software evolves, each passing test reaffirms that your product still works properly. However, in today’s fast-paced development world, testing often falls behind. This is where generative AI can aid developers as a valuable resource." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)
"Unlike traditional code completion, which operates on predefined rules, generative AI creates a continuous improvement cycle, which includes the following five basic steps: (1) Developer input: You provide source code, comments, or natural language requirements. (2) Context analysis: The model analyzes patterns in your existingcode and requirements. (3) Prediction: Based on training data and your specific context, the model generates probable code. (4) Developer feedback: You accept, modify, or reject suggestions. (5) Model adaptation: The system incorporates your feedback to improve future suggestions." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)
"This ability to zero in on important code is why modern AI coding assistants can offer meaningful suggestions for your specific needs. It’s similar to how skilled developers know which code sections affect a new implementation the most. Each transformer layer learns about various code patterns, ranging from syntax validation to understanding the relationships among functions, classes, and modules." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)
"When building new software, the clarity and precision of project requirements are pivotal. Getting the requirements right is critical as they often determine whether a software project meets its deadlines or faces significant delays. Requirements always change. Also, they’re frequently misinterpreted because we tend to grab the requirements and get to work. There is a lot of room for error here, so if we rush, we can get in trouble. Because generative AI tools make the requirements gathering process easier and faster, we can spend more time working on those requirements and getting them right." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)
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