You can build an AI reply system for an online store by retrieving relevant, current product and policy information from PostgreSQL with pgvector, using it to draft an answer, and asking a staff member to review the draft before sending. Make it “self-improving” through a controlled loop: capture staff feedback, correct authoritative source data when facts are wrong, and test proposed changes against a reviewed evaluation set. pgvector supplies similarity search; it does not keep store facts current or make replies safe to send on its own.
What the system should do—and what it should not do
A useful first version is a staff-facing drafting tool, not an autonomous support agent. For each customer question, it finds relevant approved information, presents a draft with its supporting sources, and lets a person edit, reject, or send it. This makes the source of an answer inspectable and keeps a human responsible for customer-facing claims.
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Semantic retrieval is appropriate for relatively stable text such as product descriptions, FAQs, return policies, and approved support answers. It is not a live source for changing transactional facts. Order status, current inventory, refund eligibility, and delivery state should come from their authoritative application systems. Use deterministic business rules for actions such as refunds or cancellations; a close text match is neither authorization nor proof that a transaction qualifies.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsShopify’s documentation describes a similar boundary for its own AI-generated suggested replies: drafts depend on store information, may not appear when enough relevant information is unavailable, and should be read closely and edited before sending. Those details describe Shopify Inbox, not a requirement for every custom system. Shopify also says the feature is English-only and lists a maximum of 200,000 products among its eligibility requirements. Shopify’s suggested-replies guidance
How to assemble the reply pipeline
- Choose and maintain canonical sources. Collect product details, policies, FAQs, and approved prior answers from the systems that own them. Normalize the records, retain source identifiers and revision dates, and decide how a source owner corrects errors.
- Prepare text for retrieval. Split longer documents into useful passages rather than embedding an entire policy as one opaque block. Keep metadata with each passage: source, version, product or policy scope, locale, and any tenant boundary needed for access control.
- Create embeddings and store them with the source data. Store each vector alongside its text and metadata in PostgreSQL using pgvector. Re-index or refresh records when their canonical content changes; an embedding represents the text that was processed, not whatever the source says today.
- Retrieve context for the incoming question. Apply required access and business filters, then use similarity search to find potentially relevant passages. Preserve the retrieved source IDs and versions so a reviewer can inspect what informed a draft.
- Draft with explicit limits. Provide the question and retrieved passages to the language model. Instruct it to answer only from supported information, distinguish unknowns from known facts, and ask for staff help when sources are missing, stale, or conflicting. Fetch live transactional facts separately if the reply needs them.
- Put review in the sending workflow. Show the draft and supporting sources to the staff member. Let them accept, edit, or reject it, and record the outcome without treating every edit as a verified change to policy or product truth.
This is a recommended architecture, not a documented account of a particular deployed system. Shopify’s guidance likewise ties generated replies to store information and notes that staff feedback on replies is distinct from changes to the underlying product details, policies, pages, or knowledge base. Suggested replies · Managing customer conversations
How to choose a pgvector search strategy
pgvector performs exact nearest-neighbor search by default. Its approximate indexes trade some recall for search performance, so start with exact results as a reference if the workload permits, then measure whether an approximate index is worthwhile for your corpus and traffic. The project documents two main index choices:
| Search approach | Documented tradeoff | What to measure in your workload |
|---|---|---|
| Exact search | Perfect recall; no approximate-index tradeoff. | Query latency and resource use at your actual corpus size. |
| HNSW | Generally a stronger speed/recall tradeoff than IVFFlat, with longer index builds and greater memory use. | Recall versus exact results, query latency, build time, memory, and insert/update behavior. |
| IVFFlat | Faster builds and lower memory use than HNSW, but lower query performance in the documented comparison; list and probe choices depend on the data. | Recall versus exact results, query latency, build time, memory, and behavior as data changes. |
These are qualitative tradeoffs from the pgvector project documentation, not performance measurements for an e-commerce reply system. Do not assume an index or setting is optimal without comparing it against your workload and an exact-search baseline.
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Account for filters as well as similarity
Production retrieval commonly filters by tenant, product, locale, or policy scope. With approximate indexes, pgvector applies filters after index scans, so a query may return too few matching rows. The project documents iterative scans and other approaches to this problem. Test with the same filters the application will enforce, and compare returned relevant passages against exact results; an unfiltered benchmark can conceal a filtering failure.
What “self-improving” should mean
Feedback becomes useful when it leads to a reviewable change in the right place. Keep separate records for draft quality, source corrections, and evaluation results; they represent different kinds of evidence.
Capture draft feedback as a signal
Record whether staff accepted, edited, or rejected a draft, and capture a reason when practical—for example, unsupported claim, wrong tone, missing source, or policy conflict. These actions can point to a retrieval, instruction, or context problem, but an edited reply is not automatically a verified correction. Shopify describes reply edits and ratings as feedback for future responses while directing merchants to update the underlying source information when product or policy facts need correction. Shopify’s conversation-management guidance
Correct facts in their authoritative source
If a product detail or policy is wrong, route the correction to the catalog or policy owner, update the canonical record, and then refresh the indexed text and embedding. Do not let an unreviewed customer-service edit silently become a new store policy or product fact.
Evaluate changes before release
Maintain a representative set of customer questions with human-reviewed expected answers or quality labels. Score whether each answer is correct, supported by retrieved sources, policy-compliant, and appropriately willing to abstain or request a person. Re-run the same cases when changing retrieval, prompts, embeddings, or models, then inspect failures and regressions before rollout. OpenAI’s documentation describes criteria-based evaluations and human-provided ground truth, as well as dataset annotations for evaluation at scale. Working with evals · Getting started with datasets
OpenAI’s Evals guide states that existing evals become read-only on October 31, 2026, and that the platform is scheduled to shut down on November 30, 2026. Those dates are operational and may change; verify the current official guidance before choosing an evaluation workflow. OpenAI Evals guide
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Where the design can fail
- Stale or conflicting source material: Similarity search can retrieve old text that resembles the question. Track versions, retire superseded records, and show source provenance to reviewers.
- Missing evidence: If retrieval finds no sufficiently relevant, authorized material, the system should abstain or route the question to a person rather than fill the gap with a plausible guess.
- Filters remove useful results: Approximate search combined with filters can yield too few matches. Compare filtered retrieval with exact results and use the pgvector-documented filtering approaches where needed.
- Feedback is mistaken for truth: Acceptance, edits, or ratings can inform evaluation and product improvements, but factual corrections belong in the canonical source.
- A prompt or index change causes regressions: Compare versions against the same reviewed cases and inspect errors before promoting changes broadly.
Shopify’s general AI guidance also cautions that generated outputs can contain errors and recommends providing context and reviewing generated changes. Shopify AI-powered tools
What the evidence supports
The documented architecture supports a practical way to build a staff-reviewed drafting system and a disciplined improvement loop. It does not establish that any particular implementation increased satisfaction, reduced ticket volume, improved response speed, or learned automatically from sent replies. Those outcomes require measurements from the actual system, and automatic training should not be claimed unless the implementation truly performs it.
Shopify states: “You’re responsible for the accuracy of the information that you provide your customers, even when you use automatic text generation to create it.” Shopify Help Center
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