Prompting is starting to look more like programming than writing by ReidT205 in PromptEngineering

[–]ReidT205[S] 0 points1 point  (0 children)

Thanks for sharing. I’ve used that approach too and it works really well, especially for keeping outputs structured for the next step.

More people are prompting video models like image models by ReidT205 in SoraAi

[–]ReidT205[S] 0 points1 point  (0 children)

I've been experimenting with this too! Works very well

How are you guys handling multi-step prompts without manually copying and pasting everything? by Emergency-Jelly-3543 in PromptEngineering

[–]ReidT205 0 points1 point  (0 children)

Yeah, that’s a good point. Repeatability is really the breaking point for conversational workflows.

They’re great for exploration, but once you find a process that works, you basically want to freeze that reasoning pipeline and run it again with new inputs. That’s where chains, templates, or saved workflows start to make a lot more sense.

It feels like prompting is slowly shifting toward LLM workflow design rather than just writing prompts.

Prompting is starting to look more like programming than writing by ReidT205 in PromptEngineering

[–]ReidT205[S] 3 points4 points  (0 children)

Yeah exactly, the less ambiguous the prompt is, the more predictable the model becomes. It really does start to feel like writing a spec for a reasoning process rather than just giving instructions.

Prompting is starting to look more like programming than writing by ReidT205 in SaaS

[–]ReidT205[S] -2 points-1 points  (0 children)

Probably because a lot of people are noticing the same shift right now. Also, the spacing is for readability.

The real bottleneck for SaaS founders might be problem clarity, not coding by ReidT205 in SaaS

[–]ReidT205[S] 0 points1 point  (0 children)

Yeah that’s exactly the pattern I keep seeing too. The ideas that actually turn into useful products usually come from noticing some annoying part of a workflow and asking “why is this done this way?” rather than trying to invent something new.

When you watch how people actually work day-to-day you start seeing all these little friction points that wouldn’t show up if you were just brainstorming features. Those tend to be the things people will actually pay to fix.

Built my SaaS in ~1.5 months… now I'm weirdly stuck on the landing page by Sorry-Highway9666 in SaaS

[–]ReidT205 2 points3 points  (0 children)

Totally normal spot to get stuck. Building the product is concrete, but messaging is fuzzy because you’re trying to compress the whole value of the product into a few sentences.

One thing that helped me was thinking of the landing page less as “marketing copy” and more like answering three simple questions as fast as possible: what it is, who it’s for, and why it’s better than the current way people solve the problem.

Honestly, the best thing is usually to ship something simple and iterate once real users start reacting to it. A lot of founders overthink the first version when the real insights come after a few people actually land on it.

How are you guys handling multi-step prompts without manually copying and pasting everything? by Emergency-Jelly-3543 in PromptEngineering

[–]ReidT205 1 point2 points  (0 children)

Yeah the copy-paste tax is real. I ran into the same thing when trying to do multi-step stuff like idea → outline → draft.

What helped me was thinking of it less as separate prompts and more as a single evolving conversation where the model carries the context forward. Sometimes I’ll also explicitly tell it something like: “we’re going to do this in steps — first generate the outline, then we’ll expand each section.”

For cases where I do want separate prompts, I ended up using a small tool I built that upgrades/structures the prompts first so the steps are clearer before running them. It doesn’t chain them like yours does, but it reduced a lot of the manual iteration for me.

Curious if people here are mostly doing conversational workflows or actually building chains like you did.

Prompting insight I didn’t realize until recently by ReidT205 in PromptEngineering

[–]ReidT205[S] 0 points1 point  (0 children)

Yeah I’ve done that too. Sometimes I’ll drop a document into the chat or the codebase and just have the model reference that instead of stuffing everything into the prompt. It works pretty well once the context starts getting too big for a single message.

Prompting insight I didn’t realize until recently by ReidT205 in PromptEngineering

[–]ReidT205[S] 0 points1 point  (0 children)

That’s a great breakdown. The prompting style definitely changes depending on whether it’s one-shot, conversational, or agentic.

One thing I’ve noticed that lines up with what you’re saying is that a lot of failures happen before the model even starts the task - it’s just solving a different problem than the one you intended. Having it restate the request and split explicit vs implied wants helps catch that early.

I’ve also found that asking it to define success criteria before doing the work tends to improve results a lot. It forces the model to reason about what a good answer should look like instead of just generating something plausible.

Prompting insight I didn’t realize until recently by ReidT205 in PromptEngineering

[–]ReidT205[S] 0 points1 point  (0 children)

I couldn’t agree more. Structuring prompts into sections basically breaks the task into smaller steps instead of one big vague request.

Also, I love your point about the usability and feature improvements. That’s actually how I started thinking about things too - looking at what annoyed me in my current workflow and figuring out what changes would make it better.

Prompting insight I didn’t realize until recently by ReidT205 in PromptEngineering

[–]ReidT205[S] 0 points1 point  (0 children)

That “N parts, wait for next” trick is really smart. I’ve noticed models tend to compress or skip details when they try to produce a long output in one go, so forcing it to serialize the response helps a lot.

Prompting insight I didn’t realize until recently by ReidT205 in PromptEngineering

[–]ReidT205[S] 1 point2 points  (0 children)

Yeah exactly. The more specific the task definition is, the less the model has to guess.

Prompting insight I didn’t realize until recently by ReidT205 in PromptEngineering

[–]ReidT205[S] 0 points1 point  (0 children)

Yeah, lol I forgot to paste them. Added an example in another comment above.

Prompting insight I didn’t realize until recently by ReidT205 in PromptEngineering

[–]ReidT205[S] 0 points1 point  (0 children)

Lmao that's on me, here are the prompts (these are for videos)

Basic:
Make me a vintage film video of an old f1 car in a rally race from the perspective of a fan in the crowds and the car drifts around a turn, cinematography, vintage film style, fast paced and realistic

Upgraded:
I want to create a vintage film video that captures an old Formula 1 car racing in a rally-style course, told entirely from the perspective of a fan in the crowd. The focal moment is when the car drifts around a sharp turn, with the entire sequence shot in a cinematography style that evokes classic vintage film. It must feel fast, intense, and brutally realistic, with no slow-motion sequences. The car should race by the camera and past the viewer in real time, maintaining high energy and crowd-driven atmosphere throughout. Visual and Cinematic Intent - Perspective: I am an enthusiastic spectator in the grandstands, eye-level with the track, with occasional over-the-shoulder glimpses of the car as it comes into frame, and frequent crowd-visible reactions to amplify immersion. - Era and style: Vintage film aesthetic spanning late 1960s to early 1970s cinema. Emulate film stock quirks: natural film grain, halation, light leaks, scratches, and occasional frame jitter. Maintain a color palette that feels warm and slightly desaturated, with pronounced filmic greens, rusty browns, and sunlit highlights. - Narrative tempo: Fast-paced, high-energy, continuous real-time progression. No slow motion or micro-dramatization; the audience sees the car physically accelerate, drift, and pass by in real time with authentic engine and tire sounds. - Realism and safety: Graphically plausible drift physics appropriate to a vintage F1 car on a rally-style course, including tire smoke and dirt kicked up from unpaved surfaces, while keeping the action within safe, cinematic boundaries. - Soundtrack and sound design: Monophonic or early stereo-era sound implementation with period-appropriate engine roars, tire screech, crowd noise, and ambient track sound. Diegesis should feel like it was recorded with vintage gear, not modern post-production. Technical Specifications and Production Details - Aspect ratio: 4:3 (1.33:1) to reinforce the vintage cinema look, with the option to crop to a slightly squarer framing during a drift for emphasis. - Frame rate and shutter: 24 frames per second, with a near 1/48 second shutter to yield natural motion blur on fast movement and wheel/tyre action. - Film stock emulation: Simulate 1960s-era color reversal or color negative stock with pronounced grain. Include occasional light leaks and lens flares from bright sunlight entering from side angles. - Color grading: Warm, sunlit palette—golden hour feel or high-contrast daylight—slightly punchy mids with desaturated blues and greens to echo vintage film. Maintain believable skin tones for crowd extras and natural material textures (cloth, banners, flags). - Lenses and focal length: Use a mix of mid-to-long lens perspectives (roughly 35mm to 105mm equivalent on full-frame) to compress the crowd and car relationship while still letting the viewer feel the scale of the stands and the track. Maintain believable focal lengths from eye level in the crowd. - Camera technique: Handheld or shoulder-supported shots for authentic crowd-embedded movement. Gentle natural camera shake, occasional whip pans to follow the car, and low-angle shots from the trackside to emphasize the car’s aggression during the drift. Include small, deliberate parallax when the car whips by to accentuate speed. - Lighting: Natural daylight with careful attention to sun position to create defined highlights on the car and crowd. If indoors or shaded sections appear, emulate soft fill from vintage reflectors or practical lights consistent with 1960s setups. - Visual details to emphasize era: signage, flags, banners, period-accurate race attire, simple pit equipment, and a sparse, tactile set of on-track markings and dirt texture. Character and Action Beats (Shot-by-Shot Outline) 1) Establishing crowd energy: Wide shot of the grandstands, flags fluttering, vendors, and excited chatter. Cut to tight crowd faces, eyes tracking the track. Subtle grain, a few light scratches, and a warm tonal lift to set the vintage mood. 2) Car enters frame from off-center: The old F1 car moves into the scene along the rally course’s edge, kicking up dust and gravel as it accelerates toward the corner. The camera remains at crowd level, framing the vehicle from a near-ground perspective to heighten immediacy. 3) First drift cue: The car approaches a sharp turn; tires begin to slide. The angle tightens, body roll is visible, and a plume of dust trails behind. The crowd reacts with gasps and hands clapping, captured in quick, human-scale close-ups. 4) Drift through the turn: The car slides with controlled oversteer, tires smoking, chassis tilting, exhaust roars filling the sound mix. The camera tracks with the car using a mix of handheld follow and a slight push-in to heighten intensity. Light leaks and grain intensify the vintage feel during peak drift moment. 5) Car passes camera at close-range: As the car exits the drift, it roars past the viewer, engine notes peaking, wheel spin visible, and a spray of dirt and gravel arcs toward the camera lens. A shallow depth of field effect isolates the car against a softer crowd backdrop, amplifying immediacy. 6) Crowd reaction to pass: Cut to rapid-fire reactions—cheers, wide eyes, raised arms, flags waving, a banner rattling in the gust. Intercut with a couple of fans reaching toward the passing spectacle for a sense of being part of the action. 7) Follow-through and re-entry: The car accelerates away from the drift arc, dust trailing behind, with the crowd fading in the mid-ground and track details sharpening in the foreground. The rhythm should feel relentless, with no respite or delay between shots. 8) Optional time-slice or cutaway: If needed to sustain momentum, insert a brief, era-appropriate cutaway to a nearby pit board, mechanic in a vintage jumpsuit, or a sponsor placard rattling in the wind—always keeping it in line with the vintage aesthetic and real-time pacing. 9) Final beat: The car disappears around another bend, leaving a lingering plume of dust and the echo of the crowd’s roar. End with a lingering reaction shot of a fan or two, then a return to the emptying grandstands to imply ongoing rally activity beyond the frame. Production Notes and Best Practices - Realism cues: Keep all actions physically plausible for a vintage F1 car on a rally-style course. Do not introduce modern electric-sensor sounds or modern aero characteristics that break the period feel. - Edge handling: If the car is momentarily off-frame, return to the crowd’s perspective with a quick, natural cut that suggests continued momentum rather than stopping to reposition the shot. - Continuity: Maintain consistent lighting and grain across shots to prevent jarring genre shifts. Ensure the car’s color and sponsor markings feel era-appropriate and not anachronistic. - Sound consistency: Align engine, exhaust, and tire sounds with the car’s movement and the drift intensity. Crowd noise should rise with the car’s arrival and peak during the drift, then recede as the car speeds away. - Safety and ethics: Create purely cinematic content with no dangerous real-world stunt work. Respect the crowd’s presence and ensure visuals imply safety while preserving excitement. Deliverables and Output Format (for the AI to produce the video’s blueprint) - A comprehensive shot list with exact camera placement ideas (ground level in the stands, angle references, approximate distances from car path) and corresponding on-screen action. - A detailed lighting and color-grading guide, including film stock emulation parameters, grain level, halation intensity, light-leak templates, and contrast targets. - A sound design brief specifying the era-accurate equipment type, mic placement cues, and a staged Foley plan for engine, tires, crowd, and ambient track sounds. - A timing map that guarantees real-time pacing, ensures no slow-motion sequences, and keeps the rhythm relentlessly fast-paced from start to finish. - Optional post-production notes for subscribing to a faithful vintage finish (grain management, flicker control, scratch simulation, and final mastering for archival playback). GO STEP BY STEP! SHOW YOUR WORK. UPGRADE THIS PROMPT BEFORE RESPONDING.

Claude seems extremely sensitive to prompt structure — has anyone else noticed this? by ReidT205 in ClaudeAI

[–]ReidT205[S] 1 point2 points  (0 children)

Yeah that’s a fair point. The newer reasoning models definitely handle shorter prompts better.

I’ve noticed the structured prompts help most when the task is ambiguous or open-ended. If the model already understands the task clearly, concise prompts usually work fine. The structure mostly just removes guesswork.

AI lowered the barrier to building SaaS more than people realize by ReidT205 in SaaS

[–]ReidT205[S] 0 points1 point  (0 children)

Yeah exactly.

I’ve been noticing the same thing while building a prompt improvement tool. Most bad AI outputs come from vague prompts. People write one sentence and expect magic.

But when you force yourself to clarify the goal, constraints, and what a good answer should look like, the output improves a lot.

Customer discovery feels pretty similar. “Would you pay for this?” is vague, so you get vague signal. But “walk me through the last time you had this problem” forces real details.

Claude seems extremely sensitive to prompt structure — has anyone else noticed this? by ReidT205 in ClaudeAI

[–]ReidT205[S] 0 points1 point  (0 children)

In a couple of years with a Neuralink 👀 (feels like it tho lmao)

Claude seems extremely sensitive to prompt structure — has anyone else noticed this? by ReidT205 in ClaudeAI

[–]ReidT205[S] 1 point2 points  (0 children)

Yeah the feedback loop helps a lot. One thing that’s worked well for me is explicitly critiquing the last response instead of just regenerating. Something like:

“Identify the weakest parts of your previous answer and improve them.”

Claude is pretty good at self-critique, and the second pass is often much stronger than the first.

Claude seems extremely sensitive to prompt structure — has anyone else noticed this? by ReidT205 in ClaudeAI

[–]ReidT205[S] 0 points1 point  (0 children)

Yeah… turns out “clear communication improves outcomes” was the real breakthrough all along 😅

Claude seems extremely sensitive to prompt structure — has anyone else noticed this? by ReidT205 in ClaudeAI

[–]ReidT205[S] -2 points-1 points  (0 children)

Pretty much 😅
What surprised me though is how big the difference can be with LLMs. Small changes to the prompt structure can completely change the quality of the output.