AI isn't the solution to all your problems. AI Alternatives are plenty and specific to your use case. Understanding the nature of your query is the first step for finding an alternative or using AI smart and responsibly.
What are alternatives to AI assistants?
- human experts (developers, translators, artists, ...)
- specialized algorithmic tools like linters and IDEs
- search engines like Google, Ecosia, DuckDuckGo
- documentation
- forums
- StackOverflow
- research, learning by doing
- creative and analytic thinking
Why does it matter?
AI is prone to problems affecting its output: hallucinations, incompleteness, inconsistency, and bias. AI usage is costly, and the popular free services might require expensive paid plans or downgrade to sponsored light versions at any time. AI is costly for humanity, even if it's cheap or free for you: computation, databases and training consume energy, hardware, and human assistance wasting precious resources and threatens to accelerate climate crisis and threaten established business models that provide jobs for experienced experts.
Abstract and Article Contents
- What are alternatives to AI assistants?
- Why does it matter?
- Abstract and Article Contents
- Are Algorithms better or worse than AI?
- Reasons for not using AI tools
- Does AI Harm or Benefit Learning?
- What do people use AI for?
There are numerous possible reasons for not using AI tools, so it's good to have an alternative. Alternatives to AI assistants differ based on what AI assistants are specifically used for, like text processing, debugging, inspiration, research, learning, code creation, code explanation, refactoring, and generating images. Algorithms, linters and classic coding tools can be helpful alternatives. AI alternatives like algorithms can be hard to tell apart from LLM-based AI from a users's perspective, and even a historic chatbot like Eliza can act as a "rubber duck" dialog partner to help people focus and find answers.
AI assistants based on large-language models excel at processing text, but fail to understand existing code and generate production-level code without introducing bugs. Prompt-based image generation is likely to produce low-quality output. Instead of learning how to prompt better, people should focus on their strengths and use both AI and human experts when quality matters.
Are Algorithms better or worse than AI?
It depends. Algorithms aren't better or worse than AI in general, they're different strategies and their typical use cases overlap. From the end-users' perspective, both lack transparency and their recommendations are prone to bias and manipulation. Algorithms are more predictable and much more efficient. AI is worth a try if nothing else helps. However, using a big data model to produce unreliable output violates several best practices of software development, including the rule of least power.
Overusing AI can weaken your intellect and creativity like couch potatoeing can weaken your muscles and counteract years spent in the gym or on the courts quite quickly.
Reasons for not using AI tools
- there is a more suitable tool
- the AI's answers or code doesn't help you
- you have been blocked
- you have used up your tokens
- the AI service is down
- policies (you are not allowed to use it)
- ethical and ecological concerns
- you want to practice and learn
Does AI Harm or Benefit Learning?
Using AI for learning has been discussed controversially. AI can help the clueless, but so can tutorials and documentation. In my experience, tutorials and AI can both make learners focus on irrelevant aspects and lead to an illusion of competence.
Getting good at Shoveling Dirt
Apart from its real limitations, AI assistants often fail to understand assumptions or don't understand your requirements. As an example, it took me three attempts to make the JetBrains AI assistant, which is integrated in my IDE, to consider the code snippet already highlighted in the open editor, to answer a specific question. Finding the answer without AI might have been much quicker, and practicing to code still provides more long-term value than practicing to prompt AI questions. Quoting Erik Dietrich's Surviving the Great Commoditizer, we shouldn't get "good at shovelling dirt."
What do people use AI for?
AI isn't always useless. Otherwise it wouldn't have become so popular. AI isn't only used by lazy or penniless people either. Let’s explore when it makes sense to use AI and which alternatives might be more suitable in which situation.
Text processing
Large language operations are what LLMs were made for: digesting, transforming and creating text, especially long text about topics that have already been written about extensively. You can ask the AI to analyze a text and suggest improvements based on criteria like readability, interestingness, or consistency. Asking an AI to summarize an essay's topics and central claims can be quick and easy. But even if you have no time or talent to read long text with your own eyes, there is an alternative: human experts. Pro: higher quality and accuracy, if you chose the right one. Con: experts cost time and money. But human experts often provide better value for money once you have to pay for AI and consider long-term total costs, customer conversion, or learning and practicing your professional skills.
Combining the advantages of all possible solutions, you could start with your own thoughts and take notes, then use AI or a search engine for more inspiration and aspect that you might have overlooked.
Write and let your thoughts flow. Read, edit, repeat.
Later, you can ask an AI and a human expert to review and suggest improvements. Don't rely on AI but choose an experienced human proof reader if quality is important!
Debugging and Development Support
A perceived strength of AI assistants that reminds me of ELIZA, an early chatbot that used simple text patterns to answer people who were made believe they were talking to a psychologist. That's also known as the teddy bear technique or rubber duck debugging, a method developers use to debug code or clarify their own understanding by explaining the problem out loud, often to an inanimate object or an imaginary interlocutor.
AI as an Advanced ELIZA
You can try out ELIZA at the New Jersey Institute of Technology's website here. https://web.njit.edu/~ronkowit/eliza.html
Talking about your problems and answering simple further inquiries helps thinking and finding solutions. I used to draft several bug reports and StackOverflow questions that I discarded unsent because providing the necessary details and context in a readable way, preferably with a minimal reproducible code example, sometimes revealed a solution that might seem obvious in hindsight. A recent example quoted in Meme Monday:
(Source: Steph Smith, quoted by Matt Novak, quoted on Meme Monday) stephsmithio paleofuture.bsky.social https://dev.to/best_codes/comment/2pe6p
AI folks have now discovered "thinking":
Sometimes in the process of writing a good enough prompt for ChatGPT, I end up solving my own problem, without even needing to submit it.
Alternatives:
- thinking
- analytic thinking
- write down your problem
- try to explain your problem to somebody
Inspiration, Ideation
Another perceived strength of AI that might turn into a trap: asking for help too early before trying yourself can bias your thoughts and ideas around those answers and prevent better or more creative alternatives that were already in your head - or somewhere else. The process of inspiration often seems random. Many creatives like to go for a walk in a park or a forest, or change location, sit in a café or a library or a shed in the countryside.
If you are looking for real random inspiration, you can pick a book and open random pages and underline words before opening your eyes or use a deck of Tarot cards. If that seems too random, may you don't need inspiration but you're already researching.
FAQ, Q&A, Research
Use a search engine or see Google alternatives like discussion boards and official documentation. The latter is probably the most underrated source of information at your fingertips without investing much time, money, or energy.
Commonplace advice about topics that you're not familiar with are traditionally found in books in a bookstore, online, or in a library. You might also try and find a knowledgeable person to talk to.
AI for decision making is doubtful. Where do they get their info from? Is it outdated or biased? When you research, you can at least decide from case to case if you trust the source or if it sounds shady. Fake Reviews, marketing content might make a trend sound too good to be true. Edge case problems might be irrelevant in your situation. Alternatives? Research and if possible ask people you know about their experience , inside a large company or a community.
Popular alternatives included StackOverflow but SO's guidelines forbid questions that tend to attract opinionated answers, explicitly including questions about best practice.
Documentation, again, can be a valuable authoritative source of truth, and - much like StackOverflow - ideally be the result of other experts' diligent research and discussion, even more so if that documentation is an official or a de-facto standard or most popular recommendation.
If you want to use AI for decision making, make sure to be specific. Ask critical further questions and insist that it respects both common knowledge and your specific requirements and insist that it does not neglect important aspects. Ask where it got its facts and make it search the web (might require premium paid plan) for up-to-date information.
Code Creation, Explanation and Refactoring
Coding assistance tasks range from single-line auto completion suggestions and simple contextual questions to context actions like refactoring and static code analysis to complex code generation.
The context action for "finding problems" in code has already spared me and my code reviewers unnecessary refinement rounds. Linters and static code analysis tools can be used alternatively or together with AI to improve code quality.
Maybe AI assistants will finally popularize test-driven development. Most developers don't like writing tests or documentation. AI-generated tests might be better than no tests at all, but on the other hand, they might give a false feeling of safety while testing the obvious in a naive or wrong way, and they might become a liability when maintaining a code base over time.
Developers spend more time reading code than writing it, but it's hard to anticipate edge cases and possible problems and misunderstandings in advance. Even if we try to write test-driven, type-safe, clean code with descriptive naming and concise documentation, the result often becomes legacy code sooner or later, that future developers have a hard time to understand.
Can AI help to Understand Legacy Code?
Explaining existing code needs context, analysis of existing code and recognition of common design patterns. That can't be done googling or browsing a text book. In my personal experience as a web developer, all AI tools that I have evaluated recently (in summer 2025) failed to explain legacy code and provide correct and complete answers suitable to fix or extend an existing project.
When working on legacy projects, especially those written by other developers that aren't available to answer questions anymore, my experience and intuition usually helps me more than AI does.
Complex Code Generation vs. Reading the Manual
While AI can speed up creating a proof of concept quickly to evaluate a new framework or for for throw-away for a client demo or to illustrate your requirements, don't use its code as a basis for production-level software, as it tends to introduce unnecessary technical debt and waste resources for fixing obscure bugs in the same time could be doing proper development.
Alternatives to AI-Assisted Code Generation
There are libraries of complex code often intended as boilerplate code with helpful comments, and there are wizards like the (discontinued) create-react-app. Some tech stack have a client showcase or ready-made live demos that give everyone a better impression that some AI-generated guess that seldom work properly.
In general, developers should prefer tools designed for specific tasks, following the UNIX philosophy and the principle of single responsibility.
- domain-specific code creation tools
- example code found in official documentation
- example code found on GitHub
- learning by doing
- ask or hire a senior expert
Pro: human experts can solve problems where AI assistants fail. Seniors can use their experience and intuition. Humans know about the real world and might be better at thinking beyond explicit requirements (although most can't or don't seem to care). Cons: humans make mistakes, too. Humans get tired, have an ego and you need to give them coffee or money to make them work.
Alternatives to AI-assisted Image Generation
Use charting tools to create technical diagrams. Use a graphics tablet, vector drawing software, or your own hands, to draw creative cartoons. Seriously!
I asked AI to draw a cartoon to illustrate the claim that "human programmers get tired, make mistakes, have an ego and you need to give them coffee or money to make them work." AI doesn't complain about my lazy prompt with words partially overheard in some other developers' discussion, not a real joke, and no idea about the desired outcome. If it's not too busy, it proceeds to create an image that you might mistake for a funny cartoon if you don't read the text on it.
I suspect that this OpenAI-generated cartoon is probably copying someone's style without warning me, and the crash-test-dummy-lookalike using screen and keyboard is so stupid that it doesn't work without additional text.
Google's Gemini is not much better, taking more liberties trying to be "creative" and possibly mimicking another uncredited artist's style.
AI assistants behave like the metallic robot in the second picture: "prompt received, code generated."
Effort and Laziness as Human Virtues
Make an effort! Don't neglect learning, practicing and real human interaction, and learn to be lazy! Laziness is praised as a virtue in hacker culture. Being positively lazy increases productivity, leading to better solutions, automating repetitive tasks, and refusing to do what's unnecessary (YAGNI principle: "you ain't gonna need it!") Take a break away from the computer and get some inspiration and interaction in the real world! Use your hands and dare to be inefficient! Don't strive for efficiency, strive to be effective and individual!
"I might be lazy and starving for c0ffee, but at least, I'm original!"