AI Devs Wrestling with Prompt Workflows
AI developers are finding that prompt engineering is far more complex than initially anticipated. It's not just about crafting the perfect prompt, but also about structuring the entire workflow, handling errors, and ensuring reliability, leading to significant time investment and frustration. The focus is shifting towards a more systematic, code-like approach to prompt management.
SOURCES (60)
“when I started prompt engineering, it was quite straight forward. we could still predict the context / chat history and guess how it works internally in the transformers. there were some inconsistencies due to what we now know as harness and there are some model…”
“I spent a long time thinking better AI work mostly came from better prompts. Then I started building larger workflows and ran into a different problem: a prompt can tell a model what to do, and an automation can move information between steps, but neither one automatically defines what a complete, accurate, review-ready result should look like. That is where SOPs finally clicked for me. The pattern I use now is: Define the outcome and required inputs. Document the steps before automating them. M”
“I've been experimenting with a framework that combines Prompt Engineering and Psychology to create a longitudinal behavioral map using AI. Instead of focusing on isolated daily entries, the system records events, thoughts, emotions, behaviors, lifestyle factors, and cognitive changes over time, then analyzes recurring patterns, triggers, environmental influences, and behavioral trends across previous records. The goal is not diagnosis or therapy, but to explore whether structured prompting c”
“I’ve been trying to work on a complex app, and thought I could do it in a day with something as simple as having composer 2.5 to execute and fable 5 to prompt to save money. Well so far, it’s led to so many bugs and has sent me in a circle so many times that I almost feel like giving up. Should I just have fable 5 build what I want instead of helping me prompt better? Any advice appreciated. submitted by /u/Ilovemustang69420 [link] [comments]”
“Here's my two-part question: 1) Is it possible to prompt Chat GPT (or any AI model) to choose an option (from a variety of options given in a source file) "randomly" (not actually randomly but simulating randomness)? 2) Is possible to prevent Chat GPT from choosing the same option twice in row both within the same chat thread and across new chat threads within a project? I am building a conversational AI in GPT. It's set up as a project with instructions and source files. GPT n”
“Yes. The missing loop isn’t “ship more features faster”; it’s keeping product intent legible as the surface area grows. When delivery accelerates, teams need to distinguish between a deliberate extension of an existing pattern, a time-boxed experiment, and an accidental duplicate or contradiction. That gives product, design and engineering something concrete to review without putting a heavyweight gate in front of every change. I don’t think the answer is to slow engineers down. It is to make th”
“The most useful thing in your post is buried: "the hardest part hasn't been the AI generation, it's making that operational loop not fall over." That's your actual product, and probably your moat. Every AI builder tool claims "one prompt builds your startup" — that claim is saturated and nobody believes it anymore. Almost nobody is claiming "the agent loop that doesn't die at 3am and recovers cleanly," because it's unsexy and hard to fake. I'”
“How to learn prompt engineering.Where to learn and practice. How to use efficiently. Give me suggestions video and report and road map submitted by /u/gngopi [link] [comments]”
“How to learn prompt engineering. Give me the road map submitted by /u/gngopi [link] [comments]”
“Even at temperature zero the same prompt can give you different answers, because inference stacks arent batch-invariant and on a hosted API you dont control the batch size. wrote up how i deal with that when testing llm output - repeat runs to get a pass rate instead of a flaky single result, plus structured outputs and a calibrated judge for the rest. submitted by /u/mattjcoles [link] [comments]”
“I really like Gemma4 12b a lot but man it just can't reliably do tool calls for the life of it.”
“Most prompts I write optimise one instruction. I wanted to try a different question: can a single prompt behave like a menu-driven interface that guides someone through a recurring job without them designing the whole prompt first? The result is Digita (open source, MIT). The interaction contract: Every response ends in numbered choices; the model never acts until you pick a number. Reserved keys are constant in every menu: 8 = view context, 9 = update context (restarts the steps), 0 = new task.”
“a lot of ai features look good in demos because the prompt is doing too much hidden work. if the user needs to know the perfect phrasing, the right constraints, the edge cases, and the cleanup steps, that is not really a finished feature yet. it is a model call with a nice wrapper. the product work is usually the boring layer around it: default inputs that match the user's real job examples that show what good looks like saved rules so the user does not repeat themselves clear review states”
“if you run RAG over multi-turn chat, the single biggest accuracy lever is not your answer prompt and not your reranker. it is a small condensing prompt that turns the conversation so far into one standalone, self-contained question before you embed anything. here is why it matters. on a follow-up turn the subject is usually missing from the user's literal message, and often the subject was named by the bot, not the user. think: user asks "hours?", bot answers about a specific locat”
“Prompt engineering improves the instruction given to an AI agent. But once that agent starts working across multiple steps, the prompt is only one part of the reliability problem. A strong prompt cannot tell you whether the final result is actually correct. It cannot preserve progress between sessions, prevent infinite retries, or stop an agent from confidently grading its own work. That requires a loop around the model: Define “done” using something the system can verify Set limits on retries,”
“I always see the term prompt engineering and prompt engineer but never understood what it was. I was planning to join a similar course. Is it worth it? Is there really a job called prompt engineer and what do they do? submitted by /u/SafeEvening9468 [link] [comments]”
“I like this article because it feels so pre-AI. This is what people were arguing about on HackerNews back in the day! How so very nostalgic it makes me feel.But in response to the article: no, vanilla JS is a nightmare to keep your code organized, and battle tested frameworks do quite a good job at that. It's otherwise mostly a waste of time, or an intellectual exercise at most, to build an app with vanilla JS”
“Most prompt engineering advice treats all models the same way — one framework, apply it everywhere. In practice, the specific model changes what actually works. Sharing what I've found holds up consistently with Claude specifically, since it's a good example of a model that rewards a particular kind of structure. 1. Explicit structural tags change output quality, not just readability. Separating instructions from content with tags instead of blending them into one paragraph: <instruct”
“I kept running into the same problem: I'd solve something with the help of an AI assistant, spend time debugging an issue, or make an architectural decision... and a few weeks later I couldn't remember where that information was. Most of the time I simply didn't bother saving it anywhere. So I built Kote to capture that context automatically instead of relying on me to document it. Right now it can capture AI chats (Antigravity, Codex, Claude Code, OpenCode) from the VS Code e”
“a lot of small ai features get worse because the builder adds more knobs before the workflow is trusted. more tones, more templates, more models, more export options. all useful eventually, but they do not fix the scary part: users do not know what will happen after they click generate. the boring feature that often helps more is a review queue. show the input that was used show the output clearly let the user approve, reject, or edit save rejected examples make the next output respect those rej”
“https://specrabbit.com Most discussions about giving AI coding agents architectural context focus on prompt quality - how to write better descriptions, how to structure your system prompt, how to provide enough detail without overwhelming the context window. I've been thinking about this differently: what if the problem isn't prompt quality but prompt format? Natural language is inherently ambiguous. No matter how carefully you describe your architecture, you leave gaps - and the AI fill”
“Hi everyone, I've been seeing a lot of discussion around prompt engineering, but I'm confused about the best way to actually learn it. Some people say it's just asking AI better questions, while others treat it like a serious skill that can improve productivity and even lead to career opportunities. A little about me: I have experience with SQL, Excel, and Power BI, and I'm interested in AI, automation, and eventually building AI-powered products or a business. I'm not lookin”
“I want a behavior to happen once approximately every 10 turns, without it directly prompting it in the chat, but how can I ensure it *does* happen regularly on a variable timescale without slipping into "never actually happens" or "happens every turn"? Specifically, I want a model to change the subject of conversation sometimes during chat. I want it to privately select a new or previous topic of conversation or subject then proactively initiate the new conversation. (My syst”
“hey guys working as an ai & data engineer 2 i was getting super annoyed trying to benchmark diff system prompts side by side. testing how they handle edge cases in normal chat boxes or messy python scripts is just a pain so i built prompt arena to basically let two LLMs fight it out it has a duel mode where you can bring your own system prompts and set up a live 5 turn argument between them. then an impartial ai judge steps in and grades who won the exchange based on the transcript the stack”
“He said he was just "cleaning it up a bit." Removed a few lines that seemed redundant, tightened the phrasing. Reasonable thing to do. Didn't mention it to anyone. The prompt touched 3 downstream flows we'd built earlier in 2024. By Monday we had 12 support tickets saying responses were "off" and 2 saying the product was giving wrong information on specific edge cases. Nothing technically broke, tests passed, API responded normally. Just silently worse. We had a Notio”
“Our PRs don't get approved until somebody can answer simple questions like what got better, and what might have gotten worse? That's also why we stopped reviewing prompt changes in isolation. Braintrust lets us look at the prompt version together with the evals, which has made those conversations a lot more productive.”
“The setup: I gave it a single prompt plus a reference image, and let it run. It planned the level layout on its own, built the materials, and blocked out the whole thing, a proper prototype for wall running. I did not place any geometry myself. You can try it here: https://codefizz.dev Right now it is greyboxing with simple blockout meshes, just basic cubes. But if you point it to your own ready made static meshes in the prompt, it arranges everything into the level using those assets instead. W”
In prompt engineering, history is a first-class artifact surface. If your history is unreliable — sometimes empty, sometimes invisible, sometimes a security liability waiting for the moment it goes live — you've…
“Have heard this recently and it seems to be a new game changer. Have anyone tried it? How did that work? submitted by /u/Glum_Worldliness4904 [link] [comments]”
“If by engineering problem you mean an unsolved theoretical and real problem, then okay”
“Hi everyone, I have an interview coming up for an AI prompt engineer role. They said it was gonna be technical (screen share). Any tips please? submitted by /u/NoseSudden4323 [link] [comments]”
“I work on a prompt-optimizer stack — an MCP server, a client panel, a small web product, all talking to one backend. Each piece passed its own tests. The end-to-end stack passed CI. It took me a year to ask the question I'd been avoiding: does this actually work for someone using it today? Not "did the test pass." Does it work. In modern prompt engineering, prompts are artifacts. They have histories. They version. They get evaluated, repaired, rolled forward. None of that machinery”
“Hey everyone, I’m working on an indie game idea and want to use an LLM, like Claude or GPT, to help me evaluate how well the concept fits the current market. Specifically, I’m looking for a prompt that can help me: Analyze my game idea or pitch against current genre and gameplay trends Cross-reference it with what’s working in game marketing right now, including platforms, messaging, and community-building strategies Identify gaps or opportunities, especially where the idea stands out versus whe”
“More than 40k lines of this codebase have been written entirely by hand, without AI, since 2019. Now I'm adding the power of AI (Codex and Opencode) to the development process, and it honestly speeds things up tremendously. submitted by /u/backtotheabyssgames [link] [comments]”
“Not familiar with resources but i can tell you that my rough principle is that you need double as many pms for evals than you do for normal shipping per engineer. Its extremely important and judgement heavy.”
“I usually see "prompt engineering" focus on wording the immediate instruction well. For long-running agents, the harder problem has been context engineering : what stays hot, what gets paged out, and how exact identifiers survive hundreds of turns without asking another model to rewrite memory. I just open-sourced Context Warp Drive , a deterministic context-folding engine for LLM agents. Repo: https://github.com/dogtorjonah/context-warp-drive The basic idea: Keep the raw transcript as”
“A lot of the content here is about building good context what to include, how to structure it. curious about the other side: when a long AI coding session gets interrupted or hits a limit, the carefully built context is gone and the next session starts blind. is anyone treating "context survival across sessions" as its own problem, or is re-engineering from scratch just assumed? submitted by /u/roshandxt [link] [comments]”
“I’m hosting my first Reddit AMA soon with Max Marcon, Director of Product at MongoDB, along with Mikiko Bazeley, Staff Developer Advocate, and Yang Li, Senior SA. The AMA will focus on context engineering, RAG, agents, and what it takes to build production AI apps. Disclosure: I also work at MongoDB. I’m posting because I want to bring useful, practitioner-level questions from this community into the AMA, since I’ve seen some related topics discussed here. For people designing prompts and model”
“What it is: Flows turns a build goal into a guided AI workflow — ordered prompts, context packs, checklists, and recovery prompts for when the model produces something broken. What I need: honest feedback on whether the step-by-step breakdown is actually useful mid-build, or if it gets in the way. No signup, free to browse: https://flows-ai.emergent.host/ Try searching "working signup flow" or "stripe checkout" and tell me where it lost you. I'll reply to everyone.”
“I have been thinking less in terms of "better prompts" and more in terms of durable behavior for coding agents. I open-sourced 10x: https://github.com/z3z1ma/10x It is a one-file engineering constitution for coding agents. The point is not to stuff more text into context. The point is to make the agent work differently. The file teaches the agent to clarify before coding, separate discovery from execution, externalize durable repo context, treat subagent output as a claim until verifie”
“Every AI-assisted dev session starts cold. No project context, no memory of past decisions, no understanding of what's actively in progress. Most people solve this with a single instruction file (CLAUDE.md, AGENTS.md, .cursorrules), but that approach has a ceiling. A single file doesn't give you a place to separate stable project identity from active working state from decision history. It all gets flattened into one document that either grows too long to be useful or stays too shallow t”
“I knew how to prompt to get things done but many times it takes multiple threads to solve it completely, I can I learn prompting to solve very less conversation like 1-3! submitted by /u/exploraiwithram [link] [comments]”
“I’m trying to understand what advanced AI users are actually doing beyond basic prompt engineering. I’m not looking for “magic prompts” or viral prompt formulas. I’m more interested in the deeper workflows: - context engineering - memory systems - reasoning frameworks - tool use - agents - evals - prompt testing - decision workflows - source verification - pre-mortems - reusable templates - human-in-the-loop systems For people who feel they are past the beginner/intermediate stage: What changed”
“been building mobile apps with ai tools for a while now (lovable, v0, bolt, rapidnative, cursor + expo) and the prompts that actually produce good output look nothing like what i was writing at first. sharing the patterns that finally clicked. 1. lead with the user, not the feature. ❌ "build a fitness app with workouts, tracking, and a social feed." ✅ "build an app for someone who runs 3x a week and wants to see their pace improve. home screen shows their last run and a start butt”
“Software engineers don't ship code without tests. Most AI teams ship prompt changes with zero systematic validation. The usual excuse: "prompts are hard to test." That's half true — LLM-graded evaluation has variance. But deterministic assertions on prompt structure are straightforward, fast, and completely free. You don't need an AI judge to check that a code prompt includes a function definition, or that a structured output prompt stays under 150 words. Here's what ou”
“Hi, I've spent some time trying to find a solution to make my local AI cache the system prompt (unless it is already caching and hitting a wall on every new session is a thing)... I'm using Ornith 35b, with llama.cpp, on a Strix Halo (WIN10). It works great so far with my PI agent. I have around 7.1k tokens system prompt. Every new session, the whole 7k is being processed and it takes around 10 seconds or more to do so. How can I reduce that time? I'm only using PI, so the system pro”
“DSL: Task: Type: Programming Domain: SoftwareEngineering Objective: ProduceVerifiedImplementation CSC: CognitiveStates: Perception: Inspect Reasoning: Design Execution: Code OperationalStates: Role: Programmer Mode: Pragmatic Focus: - Correctness - Architecture Memory: Procedural RuntimeParameters: Depth: High Validation: Strict Output: Code Constraints: - Concise - Deterministic - Traceable ExecutionPolicy: Intent: Priority: StructuralGoals Ignore: - LiteralInterpretationWhenConflicting Output:”
“Whenever I want AI to complete something in phases, without my intervention, it fails. I keep running into this, regardless of which model I use and I’m curious whether anyone has found a genuinely decent solution. When I say “phased,” I mean cases where the order matters because each step produces something that the next step depends on. Sometimes I also mean more loosely defined stages, but the key point is that later steps should be grounded in what actually happened earlier, not just inferre”
“Something I've been noticing lately is that building with AI often looks less like traditional programming and more like an iterative conversation. The first prompt rarely produces the final result. Most of the work is refining instructions, breaking problems into smaller pieces, testing outputs, and gradually steering the model toward something useful. That process feels surprisingly similar to debugging code, just at a different level of abstraction. I was thinking about this after seeing”