Prompt chaining = the output of one prompt becomes the input to the next. Each prompt does exactly one job. The chain produces results no single prompt can achieve reliably.
This is how LLM-powered workflows are built: not one clever prompt, but a pipeline of focused steps with quality gates between.
Step 1 — Research: "List 5 facts about serverless databases in 2025 that would genuinely surprise a senior engineer who uses traditional hosted Postgres. For each: the specific claim, one concrete example (product name, benchmark, or incident), and confidence level: high/medium/low. Omit anything below medium confidence."
→ Review. Remove low-confidence items.
Step 2 — Structure [paste Step 1]: "Create an outline for a 1,200-word technical blog post for senior backend engineers. Sections: hook (why this matters NOW), 3 main insights, practical recommendation. For each section, write one sentence describing the specific argument it makes — not just the topic."
→ Review. Does each section make a distinct argument?
Step 3 — Draft [paste Step 2]: "Write the full blog post following this outline exactly. Technical but not academic. No filler transitions. Include one concrete code example showing the difference between traditional Postgres and Neon serverless connection handling."
Step 4 — Thread [paste Step 3]: "Convert this post to a Twitter/X thread. 8–10 tweets. First tweet: a hook that works standalone. Each subsequent tweet: one insight. Final tweet: link CTA. Max 280 chars each."
Step 1 — Extract [once per resume]: "Extract structured data from this resume. Return JSON only: {name, years_exp, current_role, current_company, key_skills[], biggest_red_flag} Resume: [paste one]"
→ Run for each resume. Review all JSON objects.
Step 2 — Score [paste all Step 1 JSON + job spec]: "Score each candidate 1–10 on: technical_fit, seniority_match, trajectory. Return a ranked table: Name | Tech | Seniority | Trajectory | Total | Top_Concern"
→ Review. Does the scoring match your actual priorities?
Step 3 — Brief [top 4 candidates]: "For these 4, write a 3-sentence interviewer brief each:
Claude handles large intermediate outputs well. Use <step_N_output> tags to label where data came from.