Steven Ramirez

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[Pratik 📈 @PratikSinhatwt]

Web Development journey explained

[Steven Ramirez @StevenR41985]

If it's not the prompt: check context. Too much noise, missing info, or wrong format. Structure your input like you'd explain the task to a careful, literal-minded person.

[Steven Ramirez @StevenR41985]

Most failures are prompt issues. The model did exactly what you asked — just not what you meant. Fix the prompt before you touch anything else.

[Steven Ramirez @StevenR41985]

I can go from idea to working demo in a weekend. Not because I'm fast — because I scope ruthlessly.

Friday: one sentence describing what the demo does and who it's for. If I can't write it, I don't build yet.

[Naval @naval]

Don’t send me the report, just send me the prompt.

[Steven Ramirez @StevenR41985]

[6/6] 5. I didn't log anything. When it failed, I had no idea why. Log every input, output, and tool call from day one. You cannot debug a black box.

[Steven Ramirez @StevenR41985]

[2/6] 1. I over-engineered the pipeline before testing a single prompt. Spent days on architecture that a 20-line prompt chain eventually replaced. Validate the core logic first, always.

[Steven Ramirez @StevenR41985]

[5/6] 4. I added every tool I thought it might need. More tools = more chances to pick the wrong one. Start minimal. Add tools only when the agent demonstrably needs them.

[Steven Ramirez @StevenR41985]

Nobody tells you the real cost of building an AI product. It's not the API bill.

It's time spent debugging outputs that are 90% right. That last 10% is where you lose days. The model didn't fail — your expectations were underspecified.

[Steven Ramirez @StevenR41985]

Not every task belongs in an AI workflow. Here's the 2-question filter I use before automating anything:

1. Is the input clearly defined?
2. Is a good output recognizable without subjective judgment?

[Steven Ramirez @StevenR41985]

API or no-code AI tool? 3 questions decide it:

1. Do you need custom data or integrations?
2. Will this scale beyond a few hundred users or runs?
3. Do you need control over the model's behavior?

[Steven Ramirez @StevenR41985]

"Vibe coding" isn't a gimmick — it's a real shift in how software gets built.

You describe what you want. AI writes the code. You review, redirect, and iterate. Most people are still sleeping on how much this compresses the build cycle.

[Steven Ramirez @StevenR41985]

I've tested more AI tools than I can count. Here's what actually survived 6 months of daily use — and what got cut:

Cut: every tool that required me to live in its ecosystem. Anything where "just use our dashboard" was the answer to "how do I integrate this?" got replaced fast.

[Steven Ramirez @StevenR41985]

[4/4] Fine-tune only when you need to change model behavior or output format at scale — and prompt engineering can't get you there consistently. Fine-tuning is expensive and slow to iterate.

[Steven Ramirez @StevenR41985]

[2/4] Prompt engineering first. Always. If a well-structured prompt solves your problem, you're done. 80% of use cases never need anything beyond this step.

[Steven Ramirez @StevenR41985]

[3/4] RAG when you need the model to reference specific, current, or proprietary data it wasn't trained on. Don't fine-tune for knowledge — that's exactly what RAG is built for.

[Steven Ramirez @StevenR41985]

Same request. Completely different result. Role + format + audience + goal = prompts that consistently perform.

[Steven Ramirez @StevenR41985]

After: "You are a conversion copywriter. Write a 2-sentence product description for a B2B SaaS tool that helps ops teams track AI agent outputs. Lead with the business outcome."