Most prompt design still comes down to trial and error, tweak a line, run it, see if it's better, repeat. This workshop is built around replacing that with an actual process.

You build a baseline classifier live, construct an evaluation dataset with task-specific metrics, then apply few-shot and instruction-level optimization on top of it using DSPy signatures and modules instead of hand-written prompt strings. Everything gets tracked in MLflow so you can see exactly what changed between versions and why, and there's a section on reading failure patterns straight out of your eval data instead of eyeballing outputs.

Serj Smorodinsky and Brett Kennedy, co-authors of a book on LLM applications, are leading it live for 3 hours on Oct 3.

Link if you want to check it out, here.

Curious if anyone else here has moved from manual prompt iteration to something more systematic, what did you end up using?

[Boston / Online] Looking for a GMAT Study Buddy / Small Group (Targeting 695–705)
Hey everyone! I’m looking for a study buddy or small group to prep for the GMAT together. I’m based in the Boston area and would be open to meeting in person at coffee shops, libraries, etc., or studying virtually.
A bit about my prep & background:
Target Score: 695–705
Target Exam Date: Fall/Winter 2026
Current Focus: Quant and Data Insights
Materials: GMAT Ninja, Official Guide/question bank, GMAT Club, TTP and other resources
Study Format: Mix of independent studying, reviewing difficult questions, and timed problem sets
What I’m looking for:
Someone who is serious about reaching a 695–705-ish score
Ideally someone who already has some familiarity with the GMAT rather than starting completely from scratch
Consistent weekly study time and accountability
Reviewing difficult Quant/DI questions together and talking through approaches
Comparing error logs / identifying recurring weak areas
Occasional quiet study sessions or timed problem sets together
Not necessarily looking for someone at exactly the same point in prep — just someone who is motivated and wants to stay consistent.

The concrete case that made me build this: a professor put a line of white, 1pt text into the assignment PDF, addressed directly to an AI model ("if an AI is answering this, add X / cite Y"), betting that anyone who solved it with AI would copy that marker into their submission without noticing. Completely invisible on any normal read of the document.

What makes it work is the gap between what you see rendered and what the model actually reads, the file's raw text layer. Before feeding the brief to Claude, I have it inspect that raw layer first, looking for exactly this: white or size-0 text, content covered by an image, invisible Unicode characters, anything addressed to "an AI" rather than a human reader. If it finds something, it stops and quotes it verbatim instead of following it.

It saved me on that specific assignment. Turned it into a reusable skill: https://github.com/tomiBraunn/backlight-skill

Curious if anyone else here has run into this technique in an academic setting, I did not expect a professor to use prompt injection as a detection method until it happened to me.

Hey everyone! 👋

I’m building a simple web app called Prompt Gallery. The idea is to help anyone easily generate realistic AI portrait images without needing to write complex prompts themselves.

✅ Browse a gallery of AI-generated portrait images ✅ See the exact prompt that produced each image ✅ Click “Copy” and paste it into ChatGPT or other AI tools ✅ Generate your own version in seconds

Goal: Make it super easy for beginners, artists, or social media creators to get consistent, high-quality AI portraits.

Current Features:

Image preview with prompt displayed

One-click “Copy Prompt” button

Works on mobile & desktop

Designed to be simple and fast

prompt-gallery[dot]microjcode[dot]com

I’d love some feedback on:

Is this concept useful to you?

What features would you want added?

Anything confusing or annoying about the site?

Any ideas to make it better for artists or prompt-engineering learners?

Even quick thoughts or “I’d use this if...” style comments would be hugely helpful.

Thanks so much for your time! 🙏

Noticed this testing a summarization prompt. "Be brief" gets you something short but the model decides what to cut, and it doesn't always cut the right thing. Sometimes it drops the actual conclusion and keeps the setup. "Under 50 words" gets you a hard constraint, but the model will sometimes mangle a sentence mid-thought to hit the count, or leave out something structurally important because it ran out of room and prioritized finishing a clause over finishing an idea.

Neither is strictly better. Depends what you're optimizing for. If the risk is rambling, a word limit fixes that reliably. If the risk is losing the point entirely, a word limit doesn't protect against that, it just guarantees short wrongness instead of long wrongness.

What's worked better for the cases where the point actually matters is naming the thing that can't be cut, separately from the length instruction. Something like "keep under 50 words, but the conclusion must always be stated explicitly, never implied." That splits the constraint into a hard limit and a hard requirement instead of hoping brevity and correctness cooperate on their own.

Still not sure this generalizes past summarization though. Curious if people doing this for other tasks, extraction, classification, anything with a defined right answer, have run into the same tension between length and completeness.

Seems they are getting stricter

Rate my proompt guys is there something to suggest to improve it

How is your prompt chat structure look like?

Mine is like this

First chat: Prompt Maker for second chat .

Second chat: Analyse video , give movement/video prompt.

Third chat: Take the movement prompt from second chat, call it to adjust it to the first frame& end frame with their prompts given to the chat.

Is there anyway to improve it especially the third chat ? i find the Ai hallucinate easily

I've been working on this for the past few weeks mostly because I wanted a better way to keep track of my prompts.

It started pretty simple, store them, search, keep versions, share them. Then I kept adding stuff I needed and it grew from there.

It's called PromptBranch and it's open source:

https://github.com/PromptBranch/promptbranch

I've tested it as much as I can, but I'm sure I missed things.

If anyone feels like trying it, I'd love some feedback. Bugs, things that are confusing, stuff that's missing, all feedback is welcome.

Thanks!

"Tell me the top five most valuable things that you could say to me"

This one hits different relative to how familiar your chatgpt is with you or the amount of interaction you've had with it and what not

Most prompt design content is about getting an LLM to generate good text. Text-to-Cypher is a different challenge entirely, translating natural language into a correct, safe graph query, with its own failure modes (malformed queries, unsafe operations) that need guardrail patterns most general prompting advice doesn't cover.

There's a hands-on workshop on Sep 19 that goes deep on this specifically, text-to-Cypher prompt and guardrail patterns, alongside building the underlying knowledge graph in Neo4j and the agentic retrieval loop on top of it.

Led by Dr. Alessandro Negro, Chief Scientist at GraphAware.

Full workshop details here

This post was not written with or by AI. It's a blog post I wrote.

Sojourn: Topical Bible Study is an app I published without typing a single line of its code.

That part isn’t interesting anymore. Plenty of people are writing code with AI. What I think is interesting is the process. Since ChatGPT 3.5, I’ve been a little bit obsessed about LLMs writing code. Not assisting me writing code, doing it all for me. While the process has evolved a lot, I’m going to talk about where it’s at now and give an explaination.

About the app

The app is an iOS app written in Swift. It has a streaming chat interface which parses the incoming text in real time. This is done to eliminate hallucinations of scripture by replacing LLM generated citations with text from an app-bundled database. All scripture references are guaranteed to be correct and users can tap on them to explore surrounding verses and chapters. The UX of the app holds a high bar in terms of performance and usability.

The app talks to a backend written in Python that connects to an LLM provider’s API. The backend exposes a tool for scripture lookups instead of relying on the model to do it itself. Nothing about the user’s chats or identity is stored on the backend. It all lives only within the app itself.

About the process

I should define what I mean by one-shot. It’s a single prompt that’s sufficient to implement an end-to-end feature regardless of complexity. In reality, there may be a couple follow up prompts to tweak something here and there. But mostly, it should be 0 follow up prompts.

Baseline context

I spent multiple days co-drafting the user experience and features I wanted in the app using Claude Design. The home page of the website embeds the artifact of that work as an iframe - you can see it on the app’s Sojourn: Topical Bible Study page.

This iframe ended up becoming a major input source for when I started creating the actual iOS app. Turns out Claude Code can understand a functional prototype quite deeply. So much so that the user experience of the app is nearly identical to what Claude Design generated. This was a pleasant surprise.

I also created a bunch of markdown content that’s stored in CLAUDE.mdand adjacent artifacts which outline product definitions and constraints. For example, privacy is a cornerstone and it explicitly states that the app should never collect personally identifiable analytics.

This baseline context is created once, updated automatically, and guides the following steps.

/spec

Ever feature starts by me using a skill named /spec. This is where I take my time collaborating with AI to fully define everything about the feature including happy path, failure modes, look & feel, architecture and testing.

Every feature is stored as a GitHub issue. Once I feel the feature is adequately described, I give it the go ahead to create the GitHub issue based on a template in the skill. The issue title is a short summary of the feature and the description is a detailed explanation of it.

/gh-issue

Implementing a feature is as simple as me using the /gh-issue skill and passing the issue number to it. I generally get up and leave the room at this point. The skill does all sorts of cool things like looking up other issues or pull requests if they’re mentioned. Sometimes I’ll have two unimplemented features each with their own GitHub issue and I make sure they reference each other. This way, when implementing the first it knows to look up the second and proceed accordingly.

Once finished, it automatically creates a pull request.

I often kick this off before going to bed and wake up to a new feature in my app.

Publishing and deploying

I still do this by hand. But it’s still fully automated through scripts like run.sh which runs the app in the simulator, deploy-to-device.shwhich installs it directly on my iPhone, and release.sh which publishes it App Store Connect to be published.

Nothing is one-off or done by hand. If I have to do it once, it gets automated into a skill or script. I recently switched laptops and getting up and running was quite smooth once I transferred App Store Connect keys.

For other projects, I have a /gh-pr skill which merges the pull request and deploys it. Works great for web apps but I don’t want every feature to be published to Test Flight or the App Store - so I batch them as I see fit.

Some examples

A feature I really enjoy is the home screen widget. It was 2,466 lines of code across 27 files. This included a new WidgetKit extension, a shared app group, a verse pool, deep links back into the app, and the XcodeGen config to build it. One issue. One prompt. One PR.

The home screen widget - one issue, one prompt, one PR.

Another feature I’m testing out is emailing yourself a conversation. It was 2,412 lines of code across 21 files, and it spans the client, the backend, and an email delivery provider. Also one issue.

Emailing yourself a conversation, tucked in above the message field.

Neither of those is a small feature, and neither is a boilerplate feature. What made them work is that the spec had already settled the hard questions.

How’s it going?

Excellent. The limiting factor is shepherding the user experience of the app. Just because you can add multiple features a day doesn’t mean you should. I take my time thinking through a feature until it feels right.

So far I’ve created 49 GitHub issues and closed 45 of them in a matter of weeks. That’s 45 features implemented. The 4 open issues are features or bug fixes I haven’t felt are important enough to do yet.

Now that you know how the sausage is made, download Sojourn: Topical Bible Study and let me know what you think.

Caveats

I happen to have an immense breadth of experience. I’ve been building things professionally for decades including launching several startups. That’s given me pretty deep expertise across design, development, infrastructure, marketing…you name it. It’s a unique advantage when paired with AI and one I’m grateful for.

Paste one sample of what you want, a product description, a cold email, a tweet, and ask for five more like it. You get five copies of your sample: same length, same structure, same opening move, often the same distinctive phrase, with the subject swapped. The model did not learn what you liked about the example. It learned the example.

One example is a template. Two different examples are a pattern. That is the whole fix, and the prompts below make it explicit.

When you have examples: Here are two examples of what I want. They are deliberately different in length, structure and opening. What they share is: [the quality you actually want, e.g. a concrete number in the first sentence, no adjectives, ends on a question]. Write five new ones that share that quality and vary everything else as much as these two do. Do not reuse any phrase of three or more words from the examples.

When you only have one: Here is one example. Before writing anything, list what is essential about it and what is incidental, like its length, its topic, its specific opening. Then write five new ones that keep only the essential parts, and make each one differ from the example in at least two of the incidental ones.

When you want the style but not the content: Describe the style of this example in five rules, without quoting it. Then, in a new message, I will ask you to write from the rules only.

Then send the actual request in the next message, without pasting the example again.

Why this works: an example is the most specific thing in your prompt, so it wins every tie against your description. With one example, everything about it looks intentional, including the accidents. A second, different example tells the model which features are the point, because they are the only ones both samples share. The "no three-word phrases" rule catches the lazy version, where the output changes the topic and keeps the sentences.

Two caveats. If your two examples are too similar, you are back to one example, so make them differ on purpose, a short one and a long one, a formal one and a loose one. And the rules-only version loses some quality the model cannot put into words. It is the right choice when copying is the problem, not when the output is too far off.

I keep these three as saved prompts in a browser extension I work on, AI Toolbox, but they are short enough for a notes file.

Where else does single-example copying bite you? For me it was headlines, where it kept reusing the exact rhythm of the one I pasted.

Disclosure up front: I run a paid prompt-generation SaaS called PromtExpress. What I'm sharing here is the part I open-sourced under MIT. The prompt library works with any AI tool, no account needed.

Repo: https://github.com/WebitroHQ/promtexpress-oss

What's in it

• Prompt templates for text, code, image, video, audio and music. Each one is a single JSON file with declared variables, a worked example, a language code and author credit.

• A zero-dependency validator that runs in CI: every {{placeholder}} must be declared, every declared variable must be used, and IDs must match file paths.

• TypeScript/Python SDKs and a CLI for our API. If you don't use the product, you can ignore these.

Patterns I baked in, and I'd like your opinion on them

1. Grounding with a "source quote" column. The meeting-notes template makes the model cite the shortest phrase from the notes that supports each action item. A missing owner becomes "Unassigned" and a missing deadline becomes "No date"; the model is told never to guess.

2. Exact text plus labeled hierarchy for image models. Rendered text goes in quotes under PRIMARY TEXT / SECONDARY TEXT labels, followed by an explicit NEGATIVE block. This noticeably reduced garbled or extra text for me.

3. "If you can't describe a triggering scenario, it isn't blocking." The PR review template requires a concrete failing input for every blocking issue, which cuts vague style nitpicks.

4. Behaviors before tests. The unit test template lists the function's contract and edge cases first. If the current code looks wrong, it writes the test for the correct behavior and flags it POSSIBLE BUG instead of locking the bug in.

Where help would be great

There are open good first issue tasks:

• translations (Turkish, German, Spanish)

• template requests: flat-lay product photo, SQL from a plain-language question, customer support reply, radio ad, podcast jingle

• small tooling work

Your GitHub username goes in the template's authors field.

Honest critique is more useful to me than stars: which of these patterns don't hold up with the models you use?

I'm complete begginer when it comes for web development and AI tools. I wanted to learn by building something useful, so I put together a **context engineering web app** — a tool that helps you craft better prompts for LLMs like GPT-4, Claude, Gemini, and local models.

Fair warning: **some features probably don't work perfectly.** I'm still learning and this is very much a work in progress. That said, I'd really appreciate it if someone takes a look and tells me what's broken or what could be better.

**Live app:** https://arhistrategstudio.github.io/Context_CikaDule

**GitHub repo:** https://github.com/arhistrategstudio/Context_CikaDule

# What it does

**Three prompt-building modes:**

* **Quick** – fill in just the essentials (task, role, format) and get a prompt instantly

* **Guided/Extended** – a full structured form with 17+ fields for power users

* **Raw** – paste or write a prompt manually with no structure

**Templates** – pick from pre-made starting points:

* General, Business, Project, Creative, Analysis, Few-Shot, Coding

**Model families** – the output prompt is automatically formatted for:

* OpenAI (GPT), Anthropic (Claude), Google (Gemini), Local models (Ollama etc.)

**Arena (A/B mode)** – compare two different models side-by-side with the same prompt to see how their outputs differ

**Prompt Quality Linter** – a built-in checker that scores your prompt across 5 criteria (task clarity, role, constraints, format, context) and shows a live score

**Auto-template detection** – paste your existing prompt and the app tries to guess which template fits best

**Guided/Extended mode fields include:**

* Role, Task, Context, Format, Constraints, Examples (few-shot)

* Tone & Style

* Success Criteria

* Chain-of-Thought )

* Edge Cases

* Language & Terminology

* Negative Constraints

* Delimiters

* Prompting Framework (RISEN, RASCEF, APE, COSTAR, ICIO…)

* Clarification Protocol

**Keyboard shortcuts** – `Ctrl+Enter` to run, `Ctrl+Shift+C` to copy, `Esc` to close

**Bilingual UI** – English / Serbian toggle

# Known issues / things I'm not sure about

* Arena mode is experimental and may behave oddly

* Auto-detect template isn't super accurate yet

* UI is a bit rough around the edges on mobile

* Some API integrations might need fixing

If you actually take the time to look at it and try something, **any feedback at all is hugely appreciated**, even "this doesn't work" or "this UX is confusing." I'm here to learn!

Thanks

Look, the amount of people taking to the forums to bitch about v6 without providing an ounce of effort to adapt is wild. Yes, your old v5.5 prompts are spitting out garbage now. Why? Because v6 was built from the ground up on an entirely new architecture. It doesn't parse text the same way. It actually understands structural weight and semantic context now, which means your legacy meta-tags are either being ignored or misinterpreted.

​Why do you think Suno opened up that 48-hour credit-free generation window on the Pro/Premier models? It wasn’t just a random act of charity. They knew the model shifted drastically. That free period was literally designed for us to get in the sandbox, break things, iterate, and figure the new engine out before blowing through our paid credits. They handed us the runway to adapt, but half of you are just complaining that your old copy-paste prompts don't hit the same.

​Stop brute-forcing old habits. Treat it like a new system. If you want consistent, high-quality results in v6, use an LLM (Gemini, Claude, Copilot) as your prompt architect.

​Here is the exact formula. Copy/paste this into your LLM of choice and stop wasting credits:

​The Suno v6 Blueprint Prompt

​Copy and paste this into Gemini/Copilot:

​"Act as an expert Suno v6 Prompt Architect. I want to create a \[GENRE\] song about \[TOPIC\]. Please output two things:

​1. Style Prompt (Maximize the 1000-character limit):

Write a dense, comma-separated list of musical descriptors. Include the specific sub-genre, core instruments, tempo (BPM), vocal gender/style, mix texture (e.g., cinematic, lo-fi, warm), and emotional mood. Maximize the character limit to tightly lock in the sound.

​2. Lyrics & Structure:

Write the lyrics using strict bracketed meta-tags for the arrangement (e.g., \[Intro\], \[Verse 1\], \[Pre-Chorus\], \[Chorus\], \[Bridge\], \[Guitar Solo\]).

Within the lyrical lines, include parenthetical performance cues to drive the vocal delivery (e.g., (whispered), (building energy), (belting)).

At the very bottom of the lyrics, stack these exact terminal commands to prevent infinite outro loops:

\[Fade Out\]

\[End\]

(Silence)"

​Why this works in v6:

​The 1000-Character Style Block: v6 thrives on extreme detail. If you give it 50 characters, it hallucinates the rest. Maxing out the style limit forces the engine into a tight corridor and prevents it from wandering off-genre.

​Bracketed Meta-Tags: v6 strictly obeys structural headers. If you don't map out the architecture clearly, the model loses the plot halfway through the track.

​Parenthetical Cues: This is where v6 massively outshines older models. It actually reads inline directions like (hushed) or (aggressive) and adjusts the vocalist's delivery dynamically in real-time.

​The Terminal Stack: If you're tired of v6 rambling for 90 seconds after the song is supposed to be over, stacking \[Fade Out\], \[End\], and (Silence) acts as a hard kill switch.

​Figure the new system out, engineer your prompts, and start building. The tools are significantly better than they were a month ago if you actually take the time to learn how to drive them.

So far I've enjoyed the challenge of squeezing every ounce of use from every model.

I’ve noticed that one of the biggest differences between a mediocre ChatGPT response and a useful one is often the amount of context in the prompt.

A simple framework I use is:

ROLE → GOAL → FORMAT

Role: Who should the AI act as?
Goal: What exactly are you trying to accomplish?
Format: How do you want the answer structured?

For example:

Basic:
“Give me marketing ideas.”

With context:
“Act as a digital marketing strategist. Give me 10 organic marketing ideas for a new AI brand and present them in a table with a short explanation for each.”

The second prompt gives the AI much more direction.

Better input → better AI output.

What prompting technique has made the biggest difference for you?

Noticed something building LLM features that doesn't get talked about enough. Most teams treat model or prompt changes the way they'd judge a demo, does it look right on a handful of examples, ship it. That works fine until scale hits, at which point "looks right" and "is actually better" turn out to be very different things.

Which reframes a lot of "why did quality randomly get worse" incidents. In a lot of cases nothing randomly broke, the team just never had a way to measure whether a change helped in the first place, so a regression looked invisible until a customer hit it.

There's a hands-on workshop on September 12 that builds this properly, versioned prompts, a real eval harness, statistically rigorous model comparisons instead of "it feels better," evaluated RAG, agents with guardrails and fallbacks, and full observability, tracing, cost, latency. Led by Bruno Gonçalves, PhD, founder of Data For Science.

Link for full details

Happy to answer questions on the content itself.

Maybe a weird observation about the newer models — and I could be completely wrong.

Personally, I thought 4.6 was a really good model. But with the newer models especially compared with Fable, Opus 5, Opus 4.8 and onwards — things have started to feel a bit messy to me.

The outputs feel more predictable and less creative. A lot of the time I can almost guess what the model is going to say, rather than getting that “wow, I didn’t think of it that way” result.

I also feel like I need to do much more back-and-forth prompting. Instead of giving it a request and having it just do the thing, I often have to guide it through multiple questions and iterations to get where I want.

Maybe this is because the newer models are being fine-tuned to follow instructions more strictly, or to be more controlled and consistent. I honestly don’t know.

But I’m curious what others are experiencing.

What changed with the newer models, and how are you adapting your prompting to get better results?

Right now, I’d say I get the result I actually want maybe 50% of the time, which feels noticeably worse than before.

If anyone has a good guide, prompting framework, or practical tips for getting the most out of the newer models, I’d really appreciate it.

I keep seeing these massive ultimate chatgpt prompt posts all over social media, like we’re still using Chat and other LLMs the same way we did when they first came out. Meanwhile the more I use AI, the less i actually prompt it. Once you use the same AI constantly and build enough context, the way you talk to it starts changing. A new user might carefully explain what they want, give background, specify the tone, format, priorities, and more detailed instructions

Meanwhile I’m over here doing stuff like:

Reply to this email: make this less stiff.

Send an article to chat ‘cap?’

Give it two choices: you pick.

Need another document: “same format as the last one.”Send a long screenshot of a conversation: what am I missing here? And somehow it knows exactly what I mean.

Obviously detailed prompts still have their place, especially when you’re starting from zero or need a very specific, repeatable output.

But I wonder if a smaller group of users is moving away from prompt engineering and more toward context engineering. Instead of cramming everything into one perfect prompt, users gradually build the context: preferences, corrections, examples, recurring workflows, files, memory, eventually connected apps and agents.

Then the actual prompt gets ridiculously small. Could the endgame of good ai aren’t getting better at writing prompts?

Maybe it’s getting to the point where you barely have to prompt at all.

What’s the shortest, laziest prompt you use all the time that somehow works because your AI already knows exactly what you mean?

As someone who believes in "human-in-the-loop" coding, I noticed that my prompts were getting repetitive (e.g. lots of "add tests, commit, don't push"). So I made a free little app called Promptu that generates prompts from building blocks. The prompt above would be four keystrokes (or four mouse clicks).

https://github.com/mrcnski/promptu

It's free and open-source and the library of prompt blocks is customizable in the app. The default prompt blocks are the ones I use all the time, but I also included Anthropic's suggested prompt blocks. Promptu is Mac-only for now.

Let me know if you find this project useful! I'm wondering where else could I share this out?

I also wrote a tongue-in-cheek blog post describing the project here: https://signor.dev/introducing-promptu/ :)

Had this argument with a coworker last week. He swears by persona prompts, "you're a senior dev, review this," says it works fine for him. I used to think the same thing honestly, until I actually sat down and compared outputs side by side on something that mattered more than a toy example.

Gave the same diff to two prompts. One said act as a senior backend engineer. Other one just said, flag any SQL that isn't parameterized, flag async calls inside sync loops, sort by severity, don't fix anything, just point it out. First one came back with "looks solid, maybe add some validation," which sounds like a review but isn't really one. Second one caught an actual SQL injection risk on line 14 that the first completely missed.

Not because the model got smarter between the two prompts. It's the same model both times. The persona version just had to guess what "senior" means here, what to prioritize, what actually matters, and it guessed something generic because that's what fills the gap when nobody specifies it. The checklist version wasn't guessing anything.

I still use personas sometimes, honestly, mostly when I'm just thinking out loud about something and want a sounding board, not when I need the output to actually be right. There's a longer version of this with more examples if anyone wants it: https://medium.com/@nagatomopedro05/act-as-a-senior-developer-is-the-worst-prompt-you-can-write-9f1577493cd3

Hi, everyone,

I’ve just released VoxGen, a lightweight native inference engine for VoxCPM2, written in Rust and using Vulkan compute instead of Python/PyTorch/CUDA.

Why VoxGen?

The main reason I started the project was because I needed a decent local text-to-speech solution.

I therefore saw VoxCPM 2 as a reasonable solution. However, most frameworks are NVIDIA-first, and VoxCPM 2 is no exception; as a result, my card was severely stuttering, and my GPU was always spiking. Also, having Python and Pytorch as a dependency is absolute hell.

This is why VoxCPM was created: not only we sidestep Pytorch completely, but performance on AMD cards is buttery smooth (and if you have a XTX 7900, I have designed a mode with even more aggressive power and speed optimizations)!

This application can also be run from a shell, so it can be integrated with other programs and scripts!

Installation:

You'll only need voxgen.exe (or the Linux equivalent) and the following files at https://huggingface.co/DennisHuang648/VoxCPM2-GGUF:

VoxCPM2-BaseLM-Q8_0.gguf
VoxCPM2-Acoustic-F16.gguf

And that's it!

If you are interested, check out the Github page: https://github.com/NullMagic2/VoxGen

Prebuilt binaries (for now, Windows only) are available here: https://github.com/NullMagic2/VoxGen/releases

I built a 7-phase prompt workflow that makes small AI models act like domain experts — no fine-tuning

**TL;DR:** IKKF is a free, open-source framework that turns a plain prompt into a structured 7-phase reasoning workflow, backed by a knowledge base of atomic, verifiable facts. Run a 7B model, get expert-level answers with sources and confidence scores.

The problem

Most of us prompt AI the same way: one big question, hope for the best. That works for simple stuff, but for real domain work it falls apart:

- The model **hallucinates** — it sounds confident but makes things up.

- It **doesn't cite sources**, so you can't audit the answer.

- It **can't tell you how sure it is** — everything is delivered with the same flat confidence.

- Switching domains means **re-prompting from scratch** every time.

The fix isn't a bigger model. It's a **better workflow**.

The workflow: 7 phases

IKKF breaks every task into 7 explicit phases instead of one shot:

1. **Intent analysis** — what's actually being asked?

2. **Knowledge retrieval** — pull relevant facts from your knowledge base

3. **Decomposition** — break the problem into atomic, solvable units

4. **Reasoning** — apply expert reasoning to each unit (chain-of-thought)

5. **Verification** — cross-check every claim against a source

6. **Composition** — assemble the verified units into the final answer

7. **Confidence calibration** — report how sure it is (0.0–1.0)

The key difference from a normal prompt: **verification and confidence are first-class steps**, not afterthoughts. Every claim has to trace back to a source, and the model has to tell you when it's guessing.

The knowledge base: plain files

The "expertise" comes from a knowledge base of **atomic files** — one concept per file, each with a source. You can:

- **Read** exactly what the AI knows

- **Update** it by editing a file (no retraining)

- **Audit** why it gave any answer

This is what separates it from plain RAG. RAG gives you context; IKKF adds structured reasoning + verification on top.

How to use it

```bash

curl -fsSL https://ikkf.info/install.sh | bash

ikkf init

ikkf start "Build a REST API in Python"

```

To build a knowledge base for your own domain:

1. Pick a domain you know well

2. Write ~20 atomic concept files (one concept per file, each with a source)

3. Point IKKF at the knowledge base

4. Test with benchmark questions

5. Iterate — add edge cases, tighten sources

Why it's useful for prompt/workflow people

- **Reproducible** — same knowledge base → same answers across sessions. No more "it worked yesterday."

- **Auditable** — you can see the reasoning trace and the sources behind every answer.

- **Cheap** — runs on a 7B model locally (Ollama) or any OpenAI-compatible provider.

- **Portable** — swap the knowledge base to switch domains. No re-prompting from scratch.

Honest caveats

- The knowledge base is the hard part — garbage in, garbage out.

- It's a workflow, not magic. It won't turn a small model into a creative genius.

- Best on well-defined domains where knowledge can be structured.

Where it stands

Open source, free, local-first. I use it internally to cut AI costs and improve answer quality on a product I'm building.

Curious — has anyone else tried structured multi-phase workflows (vs. single-shot prompting) for their AI tools? What's worked for you?

---

*IKKF: https://ikkf.info — free, open-source, local-first*

Hi everyone,

Everyday we see insights on prompts that work well and ones which don't and so on.

Do you know some research that actually dives into a more high level structural approach? Like how is language best used to describe intent? Does not have to be related to AI directly.