zackproser/combined_writing · Datasets at Hugging Face

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[Collaboratively update a microservice's database access pattern](https://chat.openai.com/share/d2f721ff

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bbec88881886)

[Collaboratively upgrading a section of my React application to use a new pattern](https://chat.openai.com/share/93993cd8

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# Large Language Models (LLMs)

## LLMs are a critical component of Generative AI

Large Language Models (LLMs) are the brains behind Generative AI, capable of understanding, generating, and manipulating language based on the patterns they've learned from extensive datasets.

Their role is pivotal in enabling machines to perform tasks that require human-like language understanding, from writing code to composing poetry.

Think of LLMs as the ultimate librarian, but with a superpower: instant recall of every book, article, and document ever written. They don't just store information; they understand context, draw connections, and create new content that's coherent and contextually relevant.

This makes LLMs invaluable in driving forward the capabilities of Generative AI, enabling it to generate content that feels surprisingly human.

## Hallucination: the primary challenge of LLMs

One of the main challenges with LLMs is "hallucination," where the model generates information that's plausible but factually incorrect or nonsensical.

This is akin to a brilliant storyteller getting carried away with their imagination. While often creative, these hallucinations can be misleading, making it crucial to use LLMs with a critical eye, especially in applications requiring high accuracy.

Hallucinations refer to when an AI like ChatGPT generates responses that seem plausible but don't actually reflect truth or reality. The system is essentially "making things up" based on patterns learned from its language data - hence "hallucinating".

The critical challenge here is that hallucination is more or less inextricable from the LLM behaviors we find valuable - and LLMs do not know when they do not know something. This is precisely why it can be so dangerous for junior or less experienced developers, for example, to blindly follow what an LLM says when they are attempting to pair code with one.

Without a sufficient understanding of the target space, its challenges and potential issues, it's possible to make a tremendous mess by following the hallucinations of an AI model.

### Why does hallucination happen? LLMs like ChatGPT have been trained on massive text datasets, but have no actual connection to the real world. They don't have human experiences or knowledge of facts.

Their goal is to produce outputs that look reasonable based on all the text they've seen. So sometimes the AI will confidently fill in gaps by fabricating information rather than saying "I don't know."

This is one of the reasons you'll often see LLMs referred to as "stochastic parrots". They are attempting to generate the next best word based on all of the words and writing they have ever seen.

### Should this impact trust in LLMs?

Yes, hallucinations mean we can't fully rely on LLMs for complete accuracy and truthfulness. They may get core ideas directionally right, but details could be invented.

Think of them more as an aid for content generation rather than necessarily fact sources. LLMs don't have true reasoning capacity comparable to humans. Approaching them with appropriate trust and skepticism is wise as capabilities continue advancing.

# GenAI meets software development: AI Dev Tools

## What is a developer's IDE?

IDE stands for Integrated Development Environment. It is a text editor designed specifically for programmers' needs. IDEs provide syntax highlighting, autocompletion of code, and boilerplate text insertion to accelerate the coding process.

Most modern IDEs are highly customizable. Through plugins and configuration changes, developers customize keyboard shortcuts, interface color themes, extensions that analyze code or connect to databases, and more based on their workflow.

Two very popular IDEs are Visual Studio Code (VSCode) from Microsoft and Neovim, which is open-source and maintained by a community of developers.

In VSCode, developers can install all sorts of plugins from a central marketplace - plugins to lint and format their code, run tests, interface with version control systems, and countless others. There is also rich support for changing the visual theme and layout.

Neovim is another IDE centered around modal editing optimized for speed and keyboard usage over mice. Its users can create key mappings to quickly manipulate files and code under-the-hood entirely from the keyboard. It embraces Vim language and edit commands for coding efficiency.

For example, the following gif demonstrates a custom IDE using `tmux` and Neovim (my personal preference):

My personal preference is to combine tmux with Neovim for a highly flexible setup that expands and contracts to the size of my current task.

Developers tend to "live in" their preferred IDE - meaning they spend a lot of time coding. Developers are also highly incentivized to tweak their IDE and add automations for common tasks in order to make themselves more efficient.

For this reason, Developers may try many different IDEs over the course of their career, but most tend to find something they're fond of and stick with it, which has implications for services that are or are not available in a given IDE.

Usually, a service or Developer-facing tool gets full support as a VSCode plugin long before an official Neovim plugin is created and maintained.

In summary, IDEs are incredibly valuable tools that can match the preferences and project needs of individual developers through customizations.

VSCode and Neovim have strong followings in their ability to adapt to diverse workflows. Developers can write code and configuration to customize the IDE until it perfectly suits their style.

## Generative AI in Software Development: Codeium vs. GitHub Copilot

Codeium and GitHub Copilot represent the cutting edge of Generative AI in software development, both leveraging LLMs to suggest code completions and solutions.

While GitHub Copilot is built on OpenAI's Codex, Codeium offers its unique AI-driven approach. The key differences lie in their integration capabilities, coding style adaptations, and the breadth of languages and frameworks they support, making each tool uniquely valuable depending on the developer's needs.

These tools, while serving the common purpose of enhancing coding efficiency through AI-assisted suggestions, exhibit distinct features and use cases that cater to different aspects of the development workflow.

[Codeium review](/blog/codeium

review)

b396

787e28e05375)

[Collaboratively update a microservice's database access pattern](https://chat.openai.com/share/d2f721ff

678a

4a2c

9963

bbec88881886)

[Collaboratively upgrading a section of my React application to use a new pattern](https://chat.openai.com/share/93993cd8

904d

456c

87fd

a786b8079d57)

# Large Language Models (LLMs)

## LLMs are a critical component of Generative AI

Large Language Models (LLMs) are the brains behind Generative AI, capable of understanding, generating, and manipulating language based on the patterns they've learned from extensive datasets.

Their role is pivotal in enabling machines to perform tasks that require human-like language understanding, from writing code to composing poetry.

Think of LLMs as the ultimate librarian, but with a superpower: instant recall of every book, article, and document ever written. They don't just store information; they understand context, draw connections, and create new content that's coherent and contextually relevant.

This makes LLMs invaluable in driving forward the capabilities of Generative AI, enabling it to generate content that feels surprisingly human.

## Hallucination: the primary challenge of LLMs

One of the main challenges with LLMs is "hallucination," where the model generates information that's plausible but factually incorrect or nonsensical.

This is akin to a brilliant storyteller getting carried away with their imagination. While often creative, these hallucinations can be misleading, making it crucial to use LLMs with a critical eye, especially in applications requiring high accuracy.

Hallucinations refer to when an AI like ChatGPT generates responses that seem plausible but don't actually reflect truth or reality. The system is essentially "making things up" based on patterns learned from its language data - hence "hallucinating".

The critical challenge here is that hallucination is more or less inextricable from the LLM behaviors we find valuable - and LLMs **do not know when they do not know something**. This is precisely why it can be so dangerous for junior or less experienced developers, for example, to blindly follow what an LLM says when they are attempting to pair code with one.

Without a sufficient understanding of the target space, its challenges and potential issues, it's possible to make a tremendous mess by following the hallucinations of an AI model.

### Why does hallucination happen? LLMs like ChatGPT have been trained on massive text datasets, but have no actual connection to the real world. They don't have human experiences or knowledge of facts.

Their goal is to produce outputs that look reasonable based on all the text they've seen. So sometimes the AI will confidently fill in gaps by fabricating information rather than saying "I don't know."

This is one of the reasons you'll often see LLMs referred to as "stochastic parrots". They are attempting to generate the next best word based on all of the words and writing they have ever seen.

### Should this impact trust in LLMs?

Yes, hallucinations mean we can't fully rely on LLMs for complete accuracy and truthfulness. They may get core ideas directionally right, but details could be invented.

Think of them more as an aid for content generation rather than necessarily fact sources. LLMs don't have true reasoning capacity comparable to humans. Approaching them with appropriate trust and skepticism is wise as capabilities continue advancing.

# GenAI meets software development: AI Dev Tools

## What is a developer's IDE?

IDE stands for Integrated Development Environment. It is a text editor designed specifically for programmers' needs. IDEs provide syntax highlighting, autocompletion of code, and boilerplate text insertion to accelerate the coding process.

Most modern IDEs are highly customizable. Through plugins and configuration changes, developers customize keyboard shortcuts, interface color themes, extensions that analyze code or connect to databases, and more based on their workflow.

Two very popular IDEs are Visual Studio Code (VSCode) from Microsoft and Neovim, which is open-source and maintained by a community of developers.

In VSCode, developers can install all sorts of plugins from a central marketplace - plugins to lint and format their code, run tests, interface with version control systems, and countless others. There is also rich support for changing the visual theme and layout.

Neovim is another IDE centered around modal editing optimized for speed and keyboard usage over mice. Its users can create key mappings to quickly manipulate files and code under-the-hood entirely from the keyboard. It embraces Vim language and edit commands for coding efficiency.

For example, the following gif demonstrates a custom IDE using `tmux` and Neovim (my personal preference):

My personal preference is to combine tmux with Neovim for a highly flexible setup that expands and contracts to the size of my current task.

Developers tend to "live in" their preferred IDE - meaning they spend a lot of time coding. Developers are also highly incentivized to tweak their IDE and add automations for common tasks in order to make themselves more efficient.

For this reason, Developers may try many different IDEs over the course of their career, but most tend to find something they're fond of and stick with it, which has implications for services that are or are not available in a given IDE.

Usually, a service or Developer-facing tool gets full support as a VSCode plugin long before an official Neovim plugin is created and maintained.

In summary, IDEs are incredibly valuable tools that can match the preferences and project needs of individual developers through customizations.

VSCode and Neovim have strong followings in their ability to adapt to diverse workflows. Developers can write code and configuration to customize the IDE until it perfectly suits their style.

## Generative AI in Software Development: Codeium vs. GitHub Copilot

Codeium and GitHub Copilot represent the cutting edge of Generative AI in software development, both leveraging LLMs to suggest code completions and solutions.

While GitHub Copilot is built on OpenAI's Codex, Codeium offers its unique AI-driven approach. The key differences lie in their integration capabilities, coding style adaptations, and the breadth of languages and frameworks they support, making each tool uniquely valuable depending on the developer's needs.

These tools, while serving the common purpose of enhancing coding efficiency through AI-assisted suggestions, exhibit distinct features and use cases that cater to different aspects of the development workflow.

[Codeium review](/blog/codeium

review)