Prompt Chaining: When One Prompt Isn’t Enough If you’ve ever tried to squeeze an entire project into one prompt—requirements → solution → plan → risks → final doc—you already know how it ends: requirements → solution → plan → risks → final doc it skips steps,
it forgets constraints,
it gives you a “confident” answer you can’t easily verify,
and the moment something is wrong, you have no idea where the mistake happened. it skips steps, it forgets constraints, it gives you a “confident” answer you can’t easily verify, and the moment something is wrong, you have no idea where the mistake happened. where Prompt Chaining is the fix. Think of it as building a workflow where each prompt is a station on an assembly line: one step in, one step out, and the output becomes the input for the next station. Prompt Chaining In other words: you’re not asking an LLM to do “everything at once.” You’re asking it to do one thing at a time, reliably. one thing at a time, reliably 1) What Is Prompt Chaining? Prompt Chaining is the practice of: Prompt Chaining Decomposing a big task into smaller sub-tasks
Designing a dedicated prompt for each sub-task
Passing structured outputs from one step to the next
Adding validation + correction steps so the chain doesn’t drift Decomposing a big task into smaller sub-tasks Decomposing Designing a dedicated prompt for each sub-task dedicated prompt for each sub-task Passing structured outputs from one step to the next Passing structured outputs Adding validation + correction steps so the chain doesn’t drift validation + correction It’s basically the “microservices mindset” applied to LLM reasoning. Single Prompt vs Prompt Chaining (in plain English) Dimension

Single Prompt

Prompt Chaining



Complexity

Good for simple, one-shot tasks

Built for multi-step, real workflows



Logic

Model guesses the process

You define the process



Control

Hard to steer

Every step is steerable



Debugging

“Where did it go wrong?”

You can pinpoint the broken step



Context limits

Easy to overflow

Feed data gradually, step-by-step Dimension

Single Prompt

Prompt Chaining



Complexity

Good for simple, one-shot tasks

Built for multi-step, real workflows



Logic

Model guesses the process

You define the process



Control

Hard to steer

Every step is steerable



Debugging

“Where did it go wrong?”

You can pinpoint the broken step



Context limits

Easy to overflow

Feed data gradually, step-by-step Dimension

Single Prompt

Prompt Chaining Dimension Dimension Single Prompt Single Prompt Prompt Chaining Prompt Chaining Complexity

Good for simple, one-shot tasks

Built for multi-step, real workflows Complexity Complexity Good for simple, one-shot tasks Good for simple, one-shot tasks Built for multi-step, real workflows Built for multi-step, real workflows Logic

Model guesses the process

You define the process Logic Logic Model guesses the process Model guesses the process You define the process You define the process define Control

Hard to steer

Every step is steerable Control Control Hard to steer Hard to steer Every step is steerable Every step is steerable Debugging

“Where did it go wrong?”

You can pinpoint the broken step Debugging Debugging “Where did it go wrong?” “Where did it go wrong?” You can pinpoint the broken step You can pinpoint the broken step Context limits

Easy to overflow

Feed data gradually, step-by-step Context limits Context limits Easy to overflow Easy to overflow Feed data gradually, step-by-step Feed data gradually, step-by-step 2) Why It Works (The Real Reason) LLMs aren’t great at juggling multiple goals simultaneously. multiple goals simultaneously Ask for: “Analyse the requirements, propose features, estimate effort, prioritise, then write a plan”—and you’ve set up a multi-objective optimisation problem. The model will usually do a decent job on one objective and quietly under-deliver on the rest. one Prompt Chaining reduces the cognitive load:   one step → one output → one success criteria. one step → one output → one success criteria 3) The Core Mechanism: Input → Process → Output (Repeated) At its heart, Prompt Chaining is a loop: Input: previous step output + any new data
Process: the next prompt with rules + format constraints
Output: structured result for the next step Input: previous step output + any new data Input: Process: the next prompt with rules + format constraints Process: Output: structured result for the next step Output: Here’s a simple chain you can visualise: flowchart LR
  A[Raw user feedback] --> B[Prompt 1: Extract pain points]
  B --> C[Prompt 2: Propose features]
  C --> D[Prompt 3: Prioritise & estimate effort]
  D --> E[Prompt 4: Write an iteration plan] flowchart LR
  A[Raw user feedback] --> B[Prompt 1: Extract pain points]
  B --> C[Prompt 2: Propose features]
  C --> D[Prompt 3: Prioritise & estimate effort]
  D --> E[Prompt 4: Write an iteration plan] 4) Four Non-Negotiables for Building Good Chains 4.1 Sub-tasks must be independent and connected and Independent: each step does one job (no overlap)
Connected: each step depends on the previous output (no “floating” steps) Independent: each step does one job (no overlap) Independent: Connected: each step depends on the previous output (no “floating” steps) Connected: Bad: “Extract pain points and design features”   Good: Step 1 extracts pain points; Step 2 designs features based on them. 4.2 Intermediate outputs must be structured Free text is fragile. The next prompt can misread it, re-interpret it, or ignore it. Use structured formats like JSON, tables, or bullet lists with fixed keys. JSON tables bullet lists with fixed keys Example (JSON you can actually parse): {
  "pain_points": [
    {"category": "performance", "description": "Checkout takes > 8 seconds", "mentions": 31},
    {"category": "ux", "description": "Refund button hard to find", "mentions": 18},
    {"category": "reliability", "description": "Payment fails with no error", "mentions": 12}
  ]
} {
  "pain_points": [
    {"category": "performance", "description": "Checkout takes > 8 seconds", "mentions": 31},
    {"category": "ux", "description": "Refund button hard to find", "mentions": 18},
    {"category": "reliability", "description": "Payment fails with no error", "mentions": 12}
  ]
} 4.3 Each prompt must explicitly “inherit” context Don’t assume the model will “remember what you meant.”   In the next prompt, explicitly refer to the previous output: “Using the pain_points JSON under pain_points, generate 1–2 features per item…” “Using the pain_points JSON under pain_points, generate 1–2 features per item…” pain_points 4.4 Build in a failure path (validation + repair) Every chain needs a “quality gate”: Validate: “Does output contain all required keys? Are numbers consistent?”
Repair: “If missing, regenerate only the missing parts”
Guardrail: “Max 2 retries; otherwise return best effort + errors” Validate: “Does output contain all required keys? Are numbers consistent?” Repair: “If missing, regenerate only the missing parts” Guardrail: “Max 2 retries; otherwise return best effort + errors” 5) Three Architectures You’ll Use Everywhere 5.1 Linear Chaining: fixed steps, no branches Use it when: the workflow is predictable. Use it when: Example: UK Monthly Revenue Report (Linear) Let’s say you have a CSV export from a UK e-commerce shop and you want: cleaning
insights
a management-ready report cleaning insights a management-ready report Step 1 — Data cleaning prompt (outputs a clean table or JSON) Step 1 — Data cleaning prompt (outputs a clean table or JSON) SYSTEM: You are a data analyst. Follow the instructions exactly.
USER:
Clean the dataset below.
​
Rules:
1) Drop rows where revenue_gbp or units_sold is null.
2) Flag outliers in revenue_gbp: > 3x category mean OR < 0.1x category mean. Do not delete them.
3) Add month_over_month_pct: (this_month - last_month) / last_month * 100.
4) Output as JSON array only. Each item must have:
   date, category, revenue_gbp, units_sold, region_uk, outlier_flag, month_over_month_pct
​
Dataset:
<PASTE DATA HERE> SYSTEM: You are a data analyst. Follow the instructions exactly.
USER:
Clean the dataset below.
​
Rules:
1) Drop rows where revenue_gbp or units_sold is null.
2) Flag outliers in revenue_gbp: > 3x category mean OR < 0.1x category mean. Do not delete them.
3) Add month_over_month_pct: (this_month - last_month) / last_month * 100.
4) Output as JSON array only. Each item must have:
   date, category, revenue_gbp, units_sold, region_uk, outlier_flag, month_over_month_pct
​
Dataset:
<PASTE DATA HERE> Step 2 — Insights prompt (outputs bullet insights) Step 2 — Insights prompt (outputs bullet insights) SYSTEM: You are a senior analyst writing for a UK leadership audience.
USER:
Using the cleaned JSON below, produce insights:
​
1) Category: Top 3 by revenue_gbp, and Top 3 by month_over_month_pct. Include contribution %.
2) Region: Top 2 regions by revenue, and biggest decline (>10%).
3) Trend: Overall trend (up/down/volatile). Explain revenue vs units relationship.
​
Output format:
- Category insights: 2-3 bullets
- Region insights: 2-3 bullets
- Trend insights: 2-3 bullets
​
Cleaned JSON:
<PASTE STEP-1 OUTPUT> SYSTEM: You are a senior analyst writing for a UK leadership audience.
USER:
Using the cleaned JSON below, produce insights:
​
1) Category: Top 3 by revenue_gbp, and Top 3 by month_over_month_pct. Include contribution %.
2) Region: Top 2 regions by revenue, and biggest decline (>10%).
3) Trend: Overall trend (up/down/volatile). Explain revenue vs units relationship.
​
Output format:
- Category insights: 2-3 bullets
- Region insights: 2-3 bullets
- Trend insights: 2-3 bullets
​
Cleaned JSON:
<PASTE STEP-1 OUTPUT> Step 3 — Report-writing prompt (outputs final document) Step 3 — Report-writing prompt (outputs final document) SYSTEM: You write crisp internal reports.
USER:
Turn the insights below into a "Monthly Revenue Brief" (800–1,000 words).
​
Structure:
1) Executive summary (1 short paragraph)
2) Key insights (Category / Region / Trend)
3) Recommendations (2–3 actionable items)
4) Close (1 short paragraph)
​
Use GBP (£) formatting and UK spelling.
Insights:
<PASTE STEP-2 OUTPUT> SYSTEM: You write crisp internal reports.
USER:
Turn the insights below into a "Monthly Revenue Brief" (800–1,000 words).
​
Structure:
1) Executive summary (1 short paragraph)
2) Key insights (Category / Region / Trend)
3) Recommendations (2–3 actionable items)
4) Close (1 short paragraph)
​
Use GBP (£) formatting and UK spelling.
Insights:
<PASTE STEP-2 OUTPUT> Linear chains are boring in the best way: they’re predictable, automatable, and easy to test. 5.2 Branching Chaining: choose a path based on classification Use it when: the next step depends on a decision (type, severity, intent). Use it when: Example: Customer message triage (Branching) Step 1 classifies the message: SYSTEM: You classify customer messages. Output only the label.
USER:
Classify this message as one of:
- complaint
- suggestion
- question
​
Output format: label: <one of the three>
​
Message:
"My order was charged but never arrived, and nobody replied to my emails. This is ridiculous." SYSTEM: You classify customer messages. Output only the label.
USER:
Classify this message as one of:
- complaint
- suggestion
- question
​
Output format: label: <one of the three>
​
Message:
"My order was charged but never arrived, and nobody replied to my emails. This is ridiculous." Then you branch: If complaint → generate incident response plan
If suggestion → produce feasibility + roadmap slotting
If question → generate a direct support answer If complaint → generate incident response plan complaint If suggestion → produce feasibility + roadmap slotting suggestion If question → generate a direct support answer question Complaint handler (example): SYSTEM: You are a customer ops manager.
USER:
Create a complaint handling plan for the message below.
​
Include:
1) Problem statement
2) Actions: within 1 hour, within 24 hours, within 48 hours
3) Compensation suggestion (reasonable for UK e-commerce)
Output in three sections with bullet points.
​
Message:
<PASTE MESSAGE> SYSTEM: You are a customer ops manager.
USER:
Create a complaint handling plan for the message below.
​
Include:
1) Problem statement
2) Actions: within 1 hour, within 24 hours, within 48 hours
3) Compensation suggestion (reasonable for UK e-commerce)
Output in three sections with bullet points.
​
Message:
<PASTE MESSAGE> Branching chains are how you stop treating every input like the same problem. 5.3 Looping Chaining: repeat until you hit a stop condition Use it when: you need to process many similar items, or refine output iteratively. Use it when: Example: Batch-generate product listings (Looping) Step 1 splits a list into item blocks: SYSTEM: You format product data.
USER:
Split the following product list into separate blocks.
​
Output format (repeat for each item):
[ITEM N]
name:
key_features:
target_customer:
price_gbp:
​
Product list:
<PASTE LIST> SYSTEM: You format product data.
USER:
Split the following product list into separate blocks.
​
Output format (repeat for each item):
[ITEM N]
name:
key_features:
target_customer:
price_gbp:
​
Product list:
<PASTE LIST> Step 2 loops over each block: SYSTEM: You write high-converting product copy.
USER:
Write an e-commerce description for the product below.
​
Requirements:
- Hook headline ≤ 12 words
- 3 feature bullets (≤ 18 words each)
- 1 sentence: best for who
- 1 sentence: why it's good value (use £)
- 150–200 words total, UK English
​
Product:
<PASTE ITEM N> SYSTEM: You write high-converting product copy.
USER:
Write an e-commerce description for the product below.
​
Requirements:
- Hook headline ≤ 12 words
- 3 feature bullets (≤ 18 words each)
- 1 sentence: best for who
- 1 sentence: why it's good value (use £)
- 150–200 words total, UK English
​
Product:
<PASTE ITEM N> Looping chains need hard stop rules: hard stop rules Process exactly N items, or
Retry at most 2 times if word count is too long, or
Stop if validation passes Process exactly N items, or Retry at most 2 times if word count is too long, or Stop if validation passes Otherwise you’ll create the world’s most expensive infinite loop. 6) Practical “Don’t Shoot Yourself” Checklist Problem: intermediate format is messy → next prompt fails Fix: make formatting non-negotiable. Fix: Add lines like: “Output JSON only.”
“If you can’t comply, output: ERROR:FORMAT.” “Output JSON only.” “If you can’t comply, output: ERROR:FORMAT.” Problem: the model forgets earlier details Fix: explicitly restate the “contract” each time. Fix: “Use the pain_points array from prior output.”
“Do not invent extra categories.” “Use the pain_points array from prior output.” pain_points “Do not invent extra categories.” Problem: loops never converge Fix: define measurable constraints + max retries. Fix: “Word count ≤ 200”
“Max retries: 2”
“If still failing, return best attempt + an error list” “Word count ≤ 200” “Max retries: 2” “If still failing, return best attempt + an error list” Problem: branch selection is wrong Fix: improve classification rules + add a second check. Fix: Example: Complaint must include negative sentiment AND a concrete issue.
If uncertain, output label: question (needs clarification). Complaint must include negative sentiment AND a concrete issue. negative sentiment AND a concrete issue If uncertain, output label: question (needs clarification). 7) Tools That Make Chaining Less Painful You can chain prompts manually (copy/paste works), but tooling helps once you go beyond a few steps. n8n / Make: low-code workflow tools for chaining API calls, storing outputs, triggering alerts.
LangChain / LangGraph: build chains with memory, branching, retries, tool calls, and state management.
Redis / Postgres: persist intermediate results so you can resume, audit, and avoid repeat calls.
Notion / Google Docs: surprisingly effective for early-stage “human in the loop” chaining. n8n / Make: low-code workflow tools for chaining API calls, storing outputs, triggering alerts. n8n / Make LangChain / LangGraph: build chains with memory, branching, retries, tool calls, and state management. LangChain / LangGraph Redis / Postgres: persist intermediate results so you can resume, audit, and avoid repeat calls. Redis / Postgres Notion / Google Docs: surprisingly effective for early-stage “human in the loop” chaining. Notion / Google Docs 8) How to Level This Up Prompt Chaining becomes even more powerful when you combine it with: RAG: add a retrieval step mid-chain (e.g., “fetch policy docs” before drafting a response)
Human approval gates: approve before risky actions (pricing changes, customer refunds, compliance replies)
Multi-modal steps: text → image brief → diagram generation → final doc RAG: add a retrieval step mid-chain (e.g., “fetch policy docs” before drafting a response) RAG Human approval gates: approve before risky actions (pricing changes, customer refunds, compliance replies) Human approval gates Multi-modal steps: text → image brief → diagram generation → final doc Multi-modal steps Final Take Prompt Chaining is not “more prompts.”   It’s workflow design. workflow design Once you start treating prompts as steps with contracts, validations, and failure paths, your LLM stops behaving like a chaotic text generator and starts acting like a dependable teammate—one station at a time. If you’re building anything beyond a one-shot demo, chain it. chain it

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Prompt Reverse Engineering: Fix Your Prompts by Studying the Wrong Answers

Prompt Chaining: Turn One Prompt Into a Reliable LLM Workflow

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