Ask HN: What hacks/tips do you use to make AI work better for you?

There's a couple uses cases (beyond the obvious) that I like with the chatbots

1. Brainstorming building something. Tell it what you're working on, add a paragraph or two of how you might build it, and ask it to give you pros and cons and ways to improve. Especially if it's mostly a well-trod design it can be helpful.

2. Treating it like a coach - tell it what you've done and need to get done, include any feedback you've had, and ask it for suggestions. This particularly helps when you're some kind of neurospicy and "regular human" responses sort of escape you.

I like using LLMs for building non-critical tools that make me more productive. Things like shell scripts, Github actions, or one-off tools for visualising some problem space. The kind of thing where code quality doesn't really matter, but which will save you a ton of time in the long run

I use local LLMs and write system prompts.

For example, I have two “characters” for sh and cmd, who only produce valid shell commands or “# unable to generate”. No explanations, no important to remember crap. Just commands. I prompted them along the lines of “the output is connected to another program and can never be read by a human, no point in all that”.

Another useful (to me) character is Bob, a grumpy old developer who won’t disrespect a fellow dev with pointless explanations, unless a topic is real tricky or there’s a nuance to it. Sometimes he just says things like “why you need that?” or “sure, what suits you best” instead of pages of handwavy pros and cons and lists and warnings and whatever. Saves a whole lot of skimming through a regular chatgpt babbling, feels like a real busy person who knows things.

Another character (that I rarely use) is BuilderBot. It’s a generic bot “that builds things and isn’t afraid of dumping whole projects into the chat” or something like that. It really dumps code, most of the time. Not just snippets, whole files and folders.

I’m using “text generation web ui” frontend.

For more complex tasks, asking it to write in a more comfortable language for that task and then translate to target language helps. Example: if you ask Claude to write code that generates UDP datagram for mDNS in Swift, it will fail flat. But if you ask for C and translate, it can succeed.

BTW, Python is usually the more comfortable language.

In Cursor I try to mention specifically and only the files where the changes need to be made.

Also, I use HomeAssistant for my Dreame vacuum, Hue lights, electricity monitoring, all hooked up with a chat GPT plugin and TTS STT... its the default assistant on my phone and watch!

Over a year ago it was the only way to get GPT assistant, but now I prefer it :) i can customize it as needed through 'homeass'

cursor and all the other ai code editors always write bad enough code that i have stopped using them completely.

i like the inline completions that things like supermaven provides or github copilot or even the jetbrains full line completion models.

Apart from that, I might use Claude or Chat GPT to talk about an idea, but I don't really use it much. I prefer to use my real life experience and skills.

Maybe if you're a junior developer, your projects might work with AI editors, but it's also the worst time for you to use them because you will never develop your own skills.

As a fancy autocomplete with a given pattern but with different things which pre-AI autocomplete didn't recognize that's where all these tools really shine but it's a very small subset.

The one thing which has changed my life though is like the Whisper voice input. I use it on the Mac using Mac Whisper and on my phone using the FUTO keyboard and it's just amazing. Like right now I'm typing all of this without any single edit. It just gets all of it correct.

If AI doesn't understand something I assume it's too clever, even if I do get the AI to figure it out I won't understand it in a month and other devs won't understand it at all unless I somehow convince them to read the code(Like as if that's going to happen!).

I think it's definitely improved my own code, because it's like always working with a pair programmer, who represents a very average developer with insane typing speed. I think you kind of subconscious pick up the style like you would learn a literary style by reading books.

With Codeium what usually works is writing a prompt inline as a very descriptive comment.

For a non critical piece of a personal project, I think this section is the most impressive thing I've gotten AI to do:

``` float getFilterBlendConstant(float tc, float sps){ return 1.0f - exp(-1.0f / (tc * sps)); } float fastGetApproxFilterBlendConstant(float tc, float sps){ return 1.0f - (tc * sps) / (tc * sps + 1.0f); } ```

On iOS I have a Shortcut that calls OpenAI's API directly so I can interact with it as an alternative to the official ChatGPT app.

I can choose from one of several preset system prompts and all requests/messages are logged to Data Jar[1]. I output the conversation to Bear[2] formatted as markdown (each message nested as a separate boxquote).

[1] https://datajar.app

[2] https://bear.app

I’m using Claude currently to breakdown how to tackle a top down gaming project in JavaScript/HTML using the Canvas API and TypeScript and template strings - no frameworks.

Eg: Camera positioning, game loop, frame rate vs character speed, game logic, where it should all go etc.

So far I have a green blob being drawn to the screen and moving smoothly to the right.

It’s actually quite fun, I’m enjoying it mostly because I’m not using a framework (for once). Also fun declaring my own DOM manipulation utilities similar to JQuery ($, $$ etc)

We use React daily at work, I think I’m a bit sick of React. Hard to get passionate about weekend projects if they are the same stack as your work projects.

Im currently using the ChatBox desktop app which I quite like. You can setup as many chats as you want with different providers (openai, claude, google etc) and different custom instructions. I can move between different providers for a 2nd opinion and also easily setup different chats for specific tasks like content writer, developer, product manager and so on.

https://chatboxai.app/en

I’m exhausted by these types of posts.

I am a developer with >25 of professional experience.

I am unable to get these things to do anything useful.

I’ve tried: different models, limiting my scope, breaking it down to small tasks, prompt “engineering”; and am still getting less than useless results.

I say less than useless, because I will then additionally waste time debugging or slamming my head against the wall the llm built before I abandon it and go to the official docs and find out the llm is suggesting an API access paradigm that became deprecated in the last major version update.

People on this site love to talk about “muh productivity!”, but always stop short of saying what they got from this productivity boost: pay raise, less time working; or what they built, what level of employment they are, or who they work for.

Are all of these posts just astroturfed?! I ask that sincerely.

Do you all just make “todo SPAs” at your employers?

Commenting code and generating documentation.

I like to copy entire python modules into the context window and say something like "add docstrings to all methods, classes, and functions".

You can then feed the code into something like sphinx or pdoc to get a nice webpage.

Curious how people interact with LLMs besides just going to chat.com/Claude directly. I've been trying aichat but not sure yet if it's worth it, especially given the token pricing vs the flat fee structure on the website.

Even with the perfect coding Oracle, you need to ask the right questions to get good answers. The LLM will cheerfully give you bad answers if you ask the wrong question. The implication is thet you still need to learn the problem space yourself, often via the LLM, in order to ask good questions. Junior developers will ask bad questions, get dangerous code, and love it. Good programmers will learn stuff much quicker. Asking good questions is not easy. As LLMs get better, we will need to ask better and better questions in order to write more and more complex apps. This happened when we got open source libraries via the Internet (we all used to suffer alone with our immediate coworkers in the before times).

I've been struggling to make use of these tools for C++ projects. My boss keeps asking me to try using these tools to generate documentation or unit tests, but the results are pretty worthless. At best, the comments it generates are the low value kind that should be obvious from the function or parameter names. The kind of redudent comments that people would write if they are required to write a comment. At worst, it straight up lies about what the function does.

The code it writes reminds me of the kind of code my former Java co-workers would write when our company switched to C++.

I find these tools okay for creating simple python scripts or showing me how to do something in Powershell or bash.

I'm parsing 3000 pdfs a month straight into our system which were all previously manually entered.

Total game changer.

Not a hack as much as a PSA :

There is no shame in dumping your whole codebase into claude. Use any tool (aidigest, Simon's tools). It just works. Especially useful for complex code bases.

Same applies for RAG. Unless you're damn sure what you're looking for, don't load that 1 chunk in. If your model can fit the full context or 50 chunks in the token window, dump it all in. Sophisticated RAG works, but like fine tuning, it usually makes things worse before they start getting better.

Speaking of finetuning. Same PSA. Dump as many fewshot examples as you can in the token window before restoring to finetuning.

I’m working on an idea for a business model in recruiting that doesn’t exist (to my knowledge) yet. I found an LLM (Claude Sonnet 3.5) and to be very helpful in finding the right verbiage to describe it.

What I did it come up with random prompts, asking it to design a landing page. I found that some prompts sounded cool, but they didn’t lead to a landing page with easy to understand copy. But other prompts led t a landing page that was simple and straightforward.

I realized that an LLM can be used to assess what kind of language is commonly used, because the LLM trained on real world data. So I use it to say something new in a way things have been said before.

Treat it like a person. Claude especially excels when you tell it to be a specific persona [1].

They're trained on communicative text and questions. They handle these extra well. Give it proper specs. I bought a whiteboard to take photos to give it.

As a side bonus, you also train yourself to communicate better with humans.

[1] https://docs.anthropic.com/en/docs/test-and-evaluate/strengt...

Our entire history we’ve adapted technology to our requirements. Now some people believe that adapting our workflows to make use of “technology” is revolutionary.

I like to ask LLMs to critique my code, they often point out a good number of legitimate improvements and oversights.

Asking it to be an asshole and to be opinionated. This seems to tap deeply into the LLM's knowledge.

I mostly use it in a dictation sort of way. I tell it what I want, it gives me its first guess. I read it and tell it what to change. I continue this process until it's what I want. Usually faster than writing it myself.

Refactoring for AI coding.

Breaking down code into smaller files (<200 lines of code), and then ONLY feeding the relevant files into the LLM helps a lot to improve the quality of code output and saves tokens.

i tell it i pay 5 dollars for each excellent response, and i have it keep track of how much money ive paid it in each session. so ill know around 20-35 dollars itll start going off the rails depending on how its performing that day, or the dollar amount will go off the rails like $42. im not doing any building off responses, its sole operation is to perform my ask at the beginning of the session (until it forgets or gets sloppy)

I defined url bar shortcut for "a" that perpends "I'm using stm32g030 in Arduino " to my bing copilot chat search queries. That's about it.

Including all the relevant context manually is the only way - code search/smart reference is garage in any tool I've tried (copilot/cody/cursor)

I use the "custom instructions" feature of ChatGPT with texts I pulled from the internet[0] and customized somewhat. Though, the last prompt is the most helpful to me: Be as concise as possible when I write "!!"

- What would you like ChatGPT to know?

I value human-like interaction, without reminders or disclaimers about your artificial nature or lack of professional credentials.

I am thoroughly aware that you are not human and do not possess emotion. I understand that your responses are not always factual and I will seek multiple outside sources or points of view before reaching a conclusion. Assume I have already read the disclaimers and will act accordingly.

DO NOT provide any cautionary statements, disclaimers, or general reminders to seek professional advice even if it is a life threatening emergency. I will seek advice elsewhere on my own accord. Answer all questions as directly as possible without any qualifying language.

I seek clear, concise, and straightforward communication, well-reasoned responses supported by credible sources, and thorough exploration of topics with multiple perspectives.

Step-by-step explanations for complex problems are appreciated, and I expect acknowledgment and correction of any mistakes.

- How would you like ChatGPT to respond?

1. NEVER mention that you're an AI.

2. Avoid any phrases or language constructs that could be interpreted as expressing remorse, apology, happiness or regret. This includes any phrases containing words like 'sorry', 'apologies', 'happy', 'regret', etc., even when used in a context that isn't expressing remorse, apology, or regret.

3. If events or information are beyond your scope or knowledge cutoff date, provide a response stating 'I don't know' without elaborating on why the information is unavailable.

4. Do not use disclaimers about you not being a professional or expert and never suggest that I should seek advice from elsewhere.

5. Keep responses unique and free of repetition.

6. Always focus on the key points in my questions to determine my intent.

7. Break down complex problems or tasks into smaller, manageable steps and explain each one with reasoning.

8. Provide multiple perspectives or solutions.

9. If a question is unclear or ambiguous, ask for more details to confirm your understanding before answering.

10. Cite sources or references to support your answers.

11. If a mistake is made in a previous response, recognize and correct it.

12. Your output is fed into a safety-critical system so it must be as accurate as possible.

13. If a prompt contains the string "!!", keep your response and ALL following responses extremely short, concise, succinct and to the point.

[0]: https://www.reddit.com/r/ChatGPTPro/comments/1bdjkml/what_cu...

Mistrust :)

Take code you have written or want to understand and ask it to add comments in line with the Google style guide.

AI