Generic prompts fail because they ask an LLM to do everything at once; these chained templates break complex tasks into verifiable steps. Prompt Chaining Techniques turn vague requests into reliable, multi-stage workflows that work consistently across Claude, GPT-4, Gemini, and DeepSeek — no more guessing where the model lost context.
Why Generic Prompts Fail Here
When you ask for a "detailed analysis" or "comprehensive plan" in one shot, the model compresses its reasoning. The failure mode is predictable: it skips edge cases, hallucinates constraints, or produces a shallow response that sounds confident but lacks depth.
Chaining fixes this by forcing the model to commit to intermediate outputs. Each stage produces a concrete artifact — a list, a draft, a set of assumptions — that the next prompt consumes. This makes errors visible early instead of buried in a 2,000-word final answer.
In my experience, the biggest win isn't accuracy. It's debuggability. When a chained pipeline produces garbage, you know exactly which stage broke and can fix that prompt alone, not rewrite the whole thing.
Template 1: The Sequential Decomposition Chain
Use this when a task has multiple distinct phases that build on each other — like writing a technical spec or planning a migration.
Stage 1 — Scope:
List all deliverables for this task: [INSERT TASK].
Output as a numbered list. Do NOT elaborate.
Each item must be independently testable.
Stage 2 — Constraints:
For each item in Stage 1, list:
- Technical constraint (framework, version, or API limit)
- Business constraint (deadline, budget, or user requirement)
- Risk (what breaks if this item fails)
Output as a markdown table.
Stage 3 — Execution Plan:
Using ONLY the items and constraints from Stages 1-2,
write a step-by-step plan. Each step must reference
at least one deliverable by its number.
If a step introduces new information not in Stages 1-2,
mark it with [ASSUMPTION] and explain why it's needed.
Placeholder meanings: [INSERT TASK] is your raw requirement. Stages 1-3 must be sent as separate messages, not combined — the model needs to see its own Stage 1 output before generating Stage 2.
Template 2: The Reverse-Validation Chain
This one's for when you already have a draft solution and need to stress-test it. It works backwards from the answer to the reasoning.
Stage 1 — Claim Extraction:
Here is my draft: [PASTE DRAFT].
Extract every factual claim, numerical figure,
and causal statement. Output as a flat list.
Number each claim.
Stage 2 — Evidence Check:
For each claim from Stage 1, respond with:
- VERIFIED: if it's common knowledge or clearly true
- UNVERIFIED: if you cannot confirm it from your training data
- CONTRADICTED: if known facts conflict with it
For UNVERIFIED items, state exactly what evidence
would be needed to confirm it.
Stage 3 — Revision:
Rewrite the original draft. For every CONTRADICTED
claim, either remove it or add a qualifier like
"according to [source]". For every UNVERIFIED claim,
add a footnote marker. Preserve the original structure.
This works well on GPT-4 and Claude because both handle the "extract then evaluate" split cleanly. Gemini tends to merge stages if you paste long drafts — keep each draft under 500 words.
Template 3: The Context-Boundary Chain
For edge cases where the model keeps drifting into irrelevant territory — like generating code when you only want architecture, or writing marketing copy when you need technical docs.
Stage 1 — Boundary Definition:
I need help with: [INSERT TASK].
List exactly 5 things this task is NOT about.
Be specific — name the adjacent topics you must avoid.
Stage 2 — Constrained Draft:
Using ONLY the task description and the 5 exclusions
from Stage 1, produce the core deliverable.
If you feel the need to mention anything from the
exclusion list, stop and write [BOUNDARY VIOLATION]
instead, then continue.
Stage 3 — Boundary Audit:
Review the Stage 2 output. For each [BOUNDARY VIOLATION]
marker, explain in one sentence why the excluded topic
was tempting to include. Then provide a clean version
with all violations removed.
The key here is Stage 1 forcing explicit exclusions. Most LLMs handle "don't do X" poorly when it's buried in a paragraph — making it a separate deliverable dramatically reduces drift.
How to Adapt These for Your Own Codebase
Don't copy these templates verbatim. Modify the stage outputs to match your project's artifacts. If you use Jira, make Stage 1 output "acceptance criteria in Given/When/Then format." If you use OpenAPI specs, make Stage 2 output "a valid YAML snippet."
Second, add your domain's vocabulary. A chain for a Django project should name models and views in Stage 1, not generic "components." The model performs better when it can anchor to concrete nouns.
Third, version your chains. Keep each template in a prompts/ directory with a changelog. When a chain fails on a new edge case, update that stage and note why. Over three months, you'll build a library that encodes your team's review process.
Do These Prompts Work With Any LLM?
Mostly, but with caveats. Claude 3.5 Sonnet and GPT-4 handle all three templates reliably because they respect multi-turn context well. Gemini 1.5 Pro works with Template 1 but sometimes merges Stage 2 and 3 if the output gets long. DeepSeek-V3 handles Template 2 exceptionally well but struggles with Template 3's boundary markers — it tends to ignore [BOUNDARY VIOLATION] and just writes the excluded content.
For best results, always paste each stage as a separate message rather than one combined prompt. The chain's power comes from the model seeing its own intermediate output, not from a single mega-prompt. This applies universally across providers.
The one adjustment that improves these prompts the most: always force the model to output a numbered list or table at every stage — it prevents rambling and makes the chain's state explicit.