Designing Cross Domain Prompt Pipelines
Given a source domain pipeline (known-working) and a target domain (new content type), map each pipeline stage 1:1 by function, not by surface content:
Source Primitive → Target Primitive (same job, different payload)
Intake & Extraction → Parse raw input into structured intermediate representation
Transformation & Mapping → Convert structure into target-domain's core "arithmetic"/logic
Compliance & Validation → Deterministic gates that catch drift/hallucination/error
Pedagogical/Final Output → Fuse validated core content with a value-add layer, format for delivery
Output a 4-stage spec doc naming each primitive, its job in source domain, its job in target domain, and the tool/model handling it (prefer cheap/fast models for extraction, deterministic code for validation, capable models reserved for synthesis).
Progress:
- Step 1: Identify the source pipeline's primitives by function (not by domain vocabulary)
- Step 2: Find the structural isomorphism in the target domain for each primitive
- Step 3: Specify the intermediate data contract between each stage (strictly typed, immutable)
- Step 4: Push all verifiable logic out of the LLM into deterministic code at the validation gate
- Step 5: Design the output primitive to add a value-multiplying layer, not just reformat
- Step 6: Write implementation notes (model choice per stage, error fallbacks, state passing)
Step 1-2 — Function-first mapping. Never map by surface similarity ("both involve PDFs"). Map by job: does this stage extract, transform, validate, or deliver? A "Geometry Node" that converts shapes into stitch counts is functionally identical to a "Research Merge" node that converts topics into content blocks — both are deterministic transformation layers sitting between raw intake and compliance checks.
Step 3 — Data contracts. Each primitive emits one well-defined payload (JSON preferred) consumed by exactly the next stage. This is what makes the pipeline "headless-safe" — no shared mutable state, no graph editor needed, just sequential function calls. State explicitly what fields are required/optional and what happens if a field is missing.
Step 4 — Deterministic validation over LLM validation. Any check with a clear pass/fail rule (sums matching, PII regex, WCAG contrast ratios, stitch-count arithmetic) belongs in code, not in a prompt. Reserve LLM calls at this stage only for semantic repair after a flagged failure (e.g., "this count is wrong, regenerate this row only").
Step 5 — Value-multiplying output. The final primitive should not just format — it should fuse the validated core artifact with an adjacent value layer (education, context, accessibility, narrative) that repositions the product in a higher-margin category. This is the actual "arbitrage": same pipeline shape, different vertical, premium repositioning via the bonus layer.
Step 6 — Production notes. Specify: which model handles which stage (fast/cheap for extraction, deterministic script for validation, higher-capability model reserved for transformation/synthesis), fallback behavior for malformed/non-standard input, and explicit statement that single-turn monolithic generation is NOT equivalent to this gated pipeline — gates must be separately invoked/verifiable steps.
Example 1: Input: "I have a working pipeline that turns legal contracts into plain-language summaries. I want to apply the same architecture to turn recipe videos into structured meal-prep plans." Output:
1. Intake & Extraction: Parse video transcript/description → structured JSON (ingredients, steps, quantities, timing)
2. Transformation & Mapping: Convert recipe steps into a meal-prep arithmetic (batch scaling, storage-life per ingredient, reheat instructions) — decouples "recipe logic" from "presentation style"
3. Compliance & Validation: Deterministic gate checks ingredient quantities sum correctly across batch scaling; allergen-flagging gate (not LLM) cross-references known allergen list
4. Output: Fuse validated meal-prep plan with a "nutrition digest" layer (macros, cost-per-serving) → formatted as print-ready shopping list + schedule
Model notes: extraction via fast model, scaling math via deterministic code, nutrition digest via higher-capability model, final render via template engine.
Example 2: Input: "Map my blog-post-to-newsletter pipeline onto generating workout plans from fitness articles." Output:
1. Intake & Extraction: Parse fitness article → exercises, sets/reps, target muscle groups
2. Transformation & Mapping: Convert into progressive-overload arithmetic (week-over-week increase logic, analogous to stitch-count increase logic) — separates "program structure" from "exercise selection style"
3. Compliance & Validation: Deterministic gate verifies total weekly volume doesn't exceed safe progression thresholds; accessibility gate converts any chart/graphic into text-based instructions
4. Output: Fuse validated workout program with "exercise science digest" (why this rep range, muscle physiology note) → premium printable program vs. generic workout sheet
- Always name primitives by function (Intake, Transform, Validate, Output) so the mapping stays portable to future domains.
- Push every checkable rule into deterministic code; LLMs should transform and repair, not arbitrate pass/fail.
- Keep each stage's output strictly typed and immutable — treat the pipeline as pure function composition, not shared state.
- Identify the "value-multiplying layer" explicitly in the output stage — this is what turns a commodity artifact into a premium one, and it's usually the actual business justification for the whole exercise.
- Call out explicitly that sequential, independently-invoked stages are not equivalent to one long single-turn prompt — the gating/verification only works if each stage is a separate, checkable call.
- Mapping by surface vocabulary instead of function (assuming two domains are analogous because both mention "PDFs" or "charts") — leads to broken isomorphisms.
- Letting the LLM "eyeball" validation instead of writing a deterministic checker — reintroduces the hallucination risk the gate was meant to remove.
- Treating accessibility/compliance as an afterthought formatting step rather than a first-class gate with its own pass/fail criteria.
- Skipping the explicit data contract between stages, causing context bloat or silent state corruption across a long pipeline.
- Forgetting the output stage's job is fusion/elevation, not mere reformatting — without the added value layer, there's no arbitrage, just a reskin.