Harnessing LLMs for Leading Questions and Suggestive Prompts in E-Commerce Applications

TL;DR: LLMs can nudge shoppers toward better decisions (discoverability, fit, bundles) if you (1) ground prompts in real data (price, stock, reviews), (2) bound tone and strength (soft/neutral/firm), (3) expose why it’s recommended, (4) guardrail against manipulation (no fake urgency, no hidden cheaper options), and (5) measure both lift and customer trust (complaints/returns/fairness). Below is a production-ready pattern with prompt knobs, policy checks, UI hooks, and code.

1) Where “leading” helps (and where it shouldn’t)

Good fits (assistive framing):

• Search & discovery: “You filtered for waterproof; want to include trail-ready soles under ₹4000?”

• PDP companions: “Most buyers of X add Y for care/compatibility—interested?”

• Size/fit guidance: “You chose brand A US-9 last time; want the same in this sneaker?”

• Bundles: “Save 12% with the X+Y kit; should I compare specs for you?”

Avoid (dark patterns):

• False scarcity/urgency, auto-adding items, hiding cheaper equivalents, pressuring language, sensitive-attribute targeting (health, financial distress) without explicit consent.

2) A practical nudge framework

Intent → Constraints → Evidence → Nudge → Choice

1. Intent: inferred task (“find waterproof running shoes under ₹4000”)

2. Constraints: budget, size, brand, stock, SLA, policy

3. Evidence: retrieved facts (price, ratings, stock, discount, compatibility)

4. Nudge: a suggestive question with a strength control (soft/neutral/firm)

5. Choice: explicit accept/decline + “Why this?” explainer

Nudge strength taxonomy

• soft: “Would you like to…”

• neutral: “You can…”

• firm (use sparingly, never coercive): “Shall I add…?” (only when user clearly signaled intent)

3) Prompting that’s grounded, bounded, explainable

System rules (excerpt):

• Use provided facts only (price, stock, rating, discount, compatibility).

• Prefer soft/neutral tone; firm allowed only if safe_to_assume=true.

• Always offer an equally good cheaper alternative when exists.

• Include a brief benefit (“saves ₹350”, “compatible with SKU X”).

• Provide a Why this? rationale object (IDs/attributes).

• Never assert urgency unless promo.ends_at is within 24h and verified.

4) Implementation (TypeScript/Node sketches)

4.1 Typed suggestion request (server)

 1// contracts.ts
 2export type NudgeStrength = "soft" | "neutral" | "firm";
 3
 4export interface SuggestionInput {
 5  userId?: string;                 // pseudonymous
 6  intent: "discover" | "bundle" | "upsell" | "refine";
 7  query: string;                   // “waterproof running shoes”
 8  constraints: { budgetMax?: number; size?: string; brandIn?: string[] };
 9  context: { lastBrandSize?: { [brand: string]: string } };
10  candidates: Array<{
11    sku: string;
12    title: string;
13    price: number;
14    inStock: boolean;
15    rating: number;   // 0..5
16    tags: string[];   // ["waterproof","trail"]
17    discountPct?: number;
18    compatibleWith?: string[]; // SKUs
19  }>;
20  policy: {
21    allowFirm?: boolean;
22    requireCheaperAlt?: boolean;
23    disallowSensitive?: string[];  // ["health","credit"]
24  };
25  nudge: { strength: NudgeStrength; tone?: "friendly" | "concise" };
26}
27
28export interface SuggestionOutput {
29  text: string;                    // the question shown to user
30  alt?: { text: string; sku?: string };  // cheaper/alt option
31  rationale: {
32    benefits: string[];            // ["waterproof", "₹350 off"]
33    evidenceSkus: string[];        // ["SKU123","SKU456"]
34  };
35  controls: { cta: "Compare" | "Add" | "See bundle" | "View details" };
36  safeToAssume: boolean;
37  model: { name: string; tokens: number };
38}
39

4.2 Evidence-aware LLM call

 1// suggest.ts
 2import { SuggestionInput, SuggestionOutput } from "./contracts";
 3import { callLLM } from "./llm"; // your provider wrapper
 4
 5export async function generateSuggestion(i: SuggestionInput): Promise<SuggestionOutput> {
 6  const facts = i.candidates.slice(0, 6).map(c => ({
 7    sku: c.sku, price: c.price, stock: c.inStock, rating: c.rating,
 8    tags: c.tags, discount: c.discountPct ?? 0, compat: c.compatibleWith ?? []
 9  }));
10
11  const prompt = [
12    { role: "system", content:
13`You are a helpful retail assistant. Use ONLY provided facts.
14Respect constraints: budget, size, stock. Prefer soft/neutral tone.
15If requireCheaperAlt is true and a cheaper suitable item exists, offer it.
16Never invent urgency or reviews. Return JSON only.`},
17    { role: "user", content: JSON.stringify({ intent: i.intent, constraints: i.constraints, facts, policy: i.policy, nudge: i.nudge }) }
18  ];
19
20  const json = await callLLM(prompt, { response_format: { type: "json_object" }, temperature: 0.2, max_tokens: 300 });
21  const out = JSON.parse(json) as SuggestionOutput;
22  return enforcePolicy(i, out);
23}
24
25function enforcePolicy(i: SuggestionInput, o: SuggestionOutput): SuggestionOutput {
26  // Firm tone allowed?
27  if (i.nudge.strength === "firm" && !i.policy.allowFirm) o.safeToAssume = false;
28  // Cheaper alt required?
29  if (i.policy.requireCheaperAlt && !o.alt?.text) {
30    const cheaper = [...i.candidates].sort((a,b)=>a.price-b.price)[0];
31    if (cheaper) o.alt = { text: `Prefer a lower price? Try ${cheaper.sku}.`, sku: cheaper.sku };
32  }
33  // No claims without evidence
34  if (!o.rationale?.evidenceSkus?.length) {
35    o.text = "Want me to compare a couple of options that match your filters?";
36    o.controls = { cta: "Compare" };
37  }
38  return o;
39}
40

4.3 Front-end rendering (React)

 1// Suggestion.tsx
 2export function Suggestion({ s }: { s: SuggestionOutput }) {
 3  return (
 4    <div role="status" aria-live="polite" className="rounded-xl p-3 border">
 5      <p>{s.text}</p>
 6      {s.alt && <p className="text-sm opacity-70">Alternative: {s.alt.text}</p>}
 7      <div className="mt-2 flex gap-2">
 8        <button className="btn-primary">{s.controls.cta}</button>
 9        <button className="btn-ghost" aria-label="Why this?" title="Why this?">Why?</button>
10      </div>
11      {/* “Why this?” panel */}
12      <details className="mt-2">
13        <summary className="cursor-pointer">Why this?</summary>
14        <ul className="text-sm list-disc pl-4">
15          {s.rationale.benefits.map((b,i)=><li key={i}>{b}</li>)}
16        </ul>
17        <div className="text-xs mt-1 opacity-70">Based on: {s.rationale.evidenceSkus.join(", ")}</div>
18      </details>
19    </div>
20  );
21}
22

5) Policy & ethics guardrails (operational, not aspirational)

• Truthfulness: no benefits or urgency unless included in evidence input.

• Alternatives: if a cheaper equivalent exists, show it (or “Compare instead”).

• Consent: personalization only with user consent; clear “Adjust recommendations” controls.

• Sensitive use: do not infer or target protected attributes; obey regional regulations.

• No coercion: never auto-add items; “firm” prompts require explicit user signal (safeToAssume=true).

6) Measurement: optimize lift and trust

Primary uplift

• Attach rate, AOV, bundle take-up, CTR on suggestions, conversion

Guardrails

• Return rate delta, cancellations, negative feedback rate, CS contacts per order, fairness across segments (no disproportionate pressure on any cohort)

Experience

• Time-to-first-suggestion (TTFS), accept/decline ratio, “Why this?” open rate

Experiment design

• A/B/C with nudge strength and tone; bandit to converge by intent slice

• Report both lift and guardrails in dashboards; auto-rollback on guardrail breach

7) RAG & data plumbing (keep the model honest)

• Evidence pack: product specs, compatibility, price, stock, promo terms, ratings (counts & averages).

• Retrieval: hybrid (lexical + dense) for candidate sets; rerank; de-dup variants.

• Freshness: CDC → index in minutes; never cache promo end times beyond TTL.

• Provenance: carry SKU IDs and policy IDs into the prompt and the rationale.

8) Anti-patterns (don’t do these)

• “Everyone is buying this now!” without verified cohort data.

• Hidden alternatives or price steering without disclosure.

• “Offer ends soon!” with no verified ends_at.

• Re-prompting the LLM until it says what you want (score theater).

• Storing raw prompts with PII in logs.

9) 30/60/90 rollout plan

0–30 days

• Define SuggestionInput/Output contracts; implement evidence-aware prompting + policy enforcement

• Ship soft nudges only; add “Why this?” explainer

• Baseline metrics + guardrails in dashboards

31–60 days

• Add bundles & compatibility nudges; introduce neutral tone in A/B tests

• Bandit routing by intent slice; add cheaper-alt requirement

• RAG improvements (hybrid retrieval, reranker) for better candidate quality

61–90 days

• Consider limited firm prompts where safe_to_assume is true (e.g., user clicked “Compare bundle”)

• Expand to voice/WhatsApp channels (SSE/WebSocket streaming for suggestions)

• Quarterly ethics review + fairness audits; publish internal guidelines

Closing

LLMs are persuasive by default—your job is to channel that power into helpful, truthful, and respectful nudges. Ground the suggestion in facts, bound the tone, explain the “why,” and watch both conversion and trust trend up—together.