How to Build an AI App with an LLM Chat Experience Inside

Start with the Use Case and Success Metrics

Before you pick a model or design a chatbot UI, get specific about what the chat experience is for. “Add an LLM chat” is not a use case—users don’t want chat, they want outcomes: answers, actions completed, and fewer back-and-forth messages.

Clarify the user problem

Write a one-sentence problem statement from the user’s point of view. For example: “I need quick, accurate answers about our return policy without opening five tabs,” or “I want to create a support ticket with the right details in under a minute.”

A helpful check: if you removed the word “chat” from the sentence and it still makes sense, you’re describing a real user need.

Pick 3–5 core tasks (and ignore the rest for now)

Keep the first version focused. Choose a small set of tasks your assistant must handle end-to-end, such as:

• Answer FAQs grounded in your official documentation
• Summarize a user’s issue and draft a support reply
• Create or update an item in your system (ticket, order, CRM record)
• Guide a user through a workflow (refund, onboarding, troubleshooting)

Each task should have a clear “done” state. If the assistant can’t reliably finish the task, it will feel like a demo rather than an AI app.

Define success metrics you can measure

Decide how you’ll know the assistant is working. Use a mix of business and quality metrics:

• Time saved: average time to complete the task vs. baseline
• Resolution rate: % of conversations that end with the user’s goal achieved
• Escalation rate: how often users still need a human
• CSAT or thumbs up/down: simple user feedback after key interactions
• Quality spot checks: sampled conversations reviewed against a rubric

Pick a starting target for each metric. Even rough targets make product decisions easier.

List constraints early (so you don’t redesign later)

Write down the boundaries that will shape everything else:

• Latency: what response time feels acceptable in your product
• Budget: cost per conversation or per active user
• Privacy and compliance: what data the model can see, store, or log
• Supported languages and tone: what “good” sounds like for your audience

With a crisp use case, a small task list, measurable metrics, and clear constraints, the rest of your LLM chat build becomes a series of practical trade-offs—not guesses.

Choose Your LLM: Hosted API vs Self-Hosted

Picking the right model is less about hype and more about fit: quality, speed, cost, and operational effort. Your choice will shape everything from user experience to ongoing maintenance.

Hosted APIs (managed models)

Hosted providers let you integrate quickly: you send text in, get text out, and they handle scaling, updates, and hardware. This is usually the best starting point for AI app development because you can iterate on your LLM chat experience without also becoming an infrastructure team.

Trade-offs: pricing can be higher at scale, data residency options may be limited, and you’re dependent on a third party’s uptime and policy constraints.

Self-hosted / open models

Running an open model yourself gives more control over data handling, customization, and potentially lower marginal cost at high volume. It can also help if you need on-prem deployment or strict governance.

Trade-offs: you own everything—model serving, GPU capacity planning, monitoring, upgrades, and incident response. Latency can be great if deployed close to users, or worse if your stack isn’t tuned.

Context window: match it to real conversations

Don’t overbuy context. Estimate typical message length and how much history or retrieved content you’ll include. Longer context windows can improve continuity, but they often increase cost and latency. For many chat flows, a smaller window plus good retrieval (covered later) is more efficient than stuffing in full transcripts.

Balancing cost, latency, and quality

For a chatbot UI, latency is a feature: users feel delays immediately. Consider a higher-quality model for complex requests and a faster/cheaper model for routine tasks (summaries, rewriting, classification).

Plan fallback models from day one

Design a simple routing strategy: a primary model, plus one or two fallbacks for outages, rate limits, or cost control. In practice, this can mean “try primary, then downgrade,” while keeping output format consistent so the rest of your app doesn’t break.

Design a Simple, Scalable Architecture

A chat experience can feel “simple” on the surface, but the app behind it needs clear boundaries. The goal is to make it easy to change models, add tools, and tighten safety controls without rewriting your UI.

Split the system into three clear layers

1) Chat UI (client layer)

Keep the front end focused on interaction patterns: streaming responses, message retry, and showing citations or tool results. Avoid placing model logic here so you can ship UI changes independently.

2) AI Service (API layer)

Create a dedicated backend service that the UI calls for /chat, /messages, and /feedback. This service should handle authentication, rate limits, and request shaping (system prompts, formatting rules). Treat it as the stable contract between your product and whatever model you use.

3) Orchestration layer (inside the AI service or as a separate service)

This is where “intelligence” becomes maintainable: tool/function calling, retrieval (RAG), policy checks, and output validation. Keeping orchestration modular lets you add capabilities—search, ticket creation, CRM updates—without entangling everything with prompt text.

If you want to move faster on the product shell (UI + backend + deployments) while you iterate on prompts, tools, and RAG, a vibe-coding platform like Koder.ai can help you generate and evolve a full-stack app from chat—then export the source code when you’re ready to take full control.

Persist the right things (not just messages)

Store conversations, but also user profiles (preferences, permissions), and events (tool calls, RAG queries, model used, latency). Event data is what makes debugging and evaluation possible later.

Build observability in from day one

Log structured payload metadata (not raw sensitive text), capture metrics (latency, token usage, tool error rates), and add tracing across UI → API → tools. When something breaks, you’ll want to answer: which step failed, for which user, and why—without guessing.

Create Prompt and Output Standards

Your chat experience will only feel “smart” if it’s also consistent. Prompt and output standards are the contract between your product and the model: what it’s allowed to do, how it should speak, and what shape the response should take so your app can reliably use it.

Define clear system instructions

Start with a system message that sets the assistant’s role, scope, and tone. Keep it specific:

• Role: “You are a support assistant for Acme Billing.”
• Scope: “Answer only about invoices, payments, and plans. If asked about unrelated topics, redirect.”
• Tone: “Friendly, concise, don’t guess; ask clarifying questions when needed.”

Avoid stuffing everything into the system message. Put stable policies and behavior there; put variable content (like user data or retrieved context) elsewhere.

Prefer structured outputs for app actions

When your UI needs to render a result (cards, tables, status labels), natural language alone becomes brittle. Use structured outputs—ideally a JSON schema—so your app can parse responses deterministically.

Example: require a response shaped like { "answer": string, "next_steps": string[], "citations": {"title": string, "url": string}[] }. Even if you don’t validate strictly at first, having a target schema reduces surprises.

Add guardrails: refusal and redirect behavior

Write explicit rules for what the assistant must refuse, what it should confirm, and what it can suggest. Include safe defaults:

• If missing key info, ask a clarifying question.
• If asked for sensitive data or disallowed requests, refuse and provide a safe alternative.
• If uncertain, say so and propose a verification step.

Create a prompt template with slots

Use a repeatable template so every request has the same structure:

• System: instructions and policies
• User: the user’s message
• Context: relevant facts (only what’s needed)
• Tools: available actions + constraints

This separation makes prompts easier to debug, evaluate, and evolve without breaking your product’s behavior.

Add Tools and Function Calling for Real Actions

A chat experience gets truly useful when it can do things: create a ticket, look up an order, schedule a meeting, or draft an email. The key is to let the model propose actions, but keep your backend in charge of what actually runs.

Decide what the AI is allowed to trigger

Start with a tight, explicit list of actions your app can safely allow, such as:

• Search internal knowledge (read-only)
• Retrieve account or order status (read-only, scoped)
• Create a support ticket or CRM note
• Draft content for review (email, announcement, checklist)
• Schedule or reschedule events (with constraints)
• Initiate a refund/credit request (never auto-approve)

If an action changes money, access, or data visibility, treat it as “risky” by default.

Use function calling for reliable operations

Rather than asking the model to “write an API request,” expose a small set of tools (functions) like get_order_status(order_id) or create_ticket(subject, details). The model chooses a tool and structured arguments; your server executes it and returns the results to continue the conversation.

This reduces errors, makes behavior more predictable, and creates clear audit logs of what was attempted.

Validate and authorize on the server

Never trust tool arguments directly. On every call:

• Validate inputs (types, formats, required fields, ranges)
• Enforce permissions (who can access what, for which customer/tenant)
• Apply rate limits and idempotency (avoid duplicate actions)

The model should suggest; your backend should verify.

Add confirmations for risky actions

For any irreversible or high-impact step, add a human-friendly confirmation: a short summary of what will happen, what data will be affected, and a clear “Confirm / Cancel” choice. For example: “I’m about to request a $50 credit for Order #1842. Confirm?”

Connect Your Data with Retrieval (RAG)

If your chat experience needs to answer questions about your product, policies, or customer history, don’t try to “bake” all that knowledge into prompts or rely on the model’s general training. Retrieval-Augmented Generation (RAG) lets the app fetch the most relevant snippets from your own content at runtime, then have the LLM answer using that context.

Decide what to retrieve vs. hardcode

A practical split is:

• Hardcode stable rules and behaviors: tone, refusal rules, formatting, and “always true” facts (e.g., support hours).
• Retrieve content that changes or is too large to fit reliably in prompts: help docs, internal wikis, release notes, pricing tables, contracts, and FAQs.

This keeps prompts simple and reduces the risk of the assistant sounding confident but wrong.

Prepare documents for high-quality retrieval

RAG quality depends heavily on preprocessing:

• Clean text: remove navigation, cookie banners, repeated footers, and broken OCR.
• Chunking: split content into small, meaningful pieces (often a few paragraphs). Chunks that are too big dilute relevance; too small lose context.
• Metadata: store fields like source URL/path, product area, version/date, audience, and access level. Metadata enables filtering (e.g., “only retrieve docs for v2”).

Choose embeddings and a vector store

You’ll generate embeddings for each chunk and store them in a vector database (or a vector-enabled search engine). Pick an embedding model that matches your language(s) and domain. Then choose a storage approach that fits your scale and constraints:

• Start simple with a managed vector store.
• Move to self-hosted if you need strict data control or custom performance tuning.

Design citations users can trust

RAG answers feel more credible when users can verify them. Return citations alongside the response: show the document title and a short excerpt, and link to the source using relative paths (e.g., /docs/refunds). If you can’t link (private docs), show a clear source label (“Policy: Refunds v3, updated 2025-09-01”).

Done well, RAG turns your LLM chat into a grounded assistant: helpful, current, and easier to audit.

Conversation Memory and Personalization

Memory is what makes an LLM chat feel like an ongoing relationship instead of a one-off Q&A. It’s also one of the easiest places to accidentally increase cost or store data you shouldn’t. Start simple and choose a strategy that matches your use case.

Pick a memory strategy

Most apps fit into one of these patterns:

• No memory: every message is treated independently. Best for sensitive topics or one-time tasks.
• Short-term memory (session): keep the recent turns (or a running summary) during an active chat. Great default for assistants and support flows.
• Long-term profile: store stable preferences (tone, timezone, product plan, “call me Alex”). Useful for personalization, but requires stronger controls.

A practical approach is short-term summary + optional long-term profile: the model stays context-aware without dragging the full transcript everywhere.

Store only what you need (and avoid sensitive data by default)

Be explicit about what you persist. Don’t save raw transcripts “just in case.” Prefer structured fields (e.g., preferred language) and avoid collecting credentials, health info, payment data, or anything you can’t justify.

If you do store memory, separate it from operational logs and set retention rules.

Summarize older turns to cut token costs

As chats grow, token usage (and latency) rises. Summarize older messages into a compact note like:

• user goal
• decisions made
• constraints and preferences
• open questions

Then keep only the latest few turns plus the summary.

Give users control

Add clear controls in the UI:

• Clear chat (ends session memory)
• Delete history (removes stored data)
• Export data (builds trust and helps support)

These small features dramatically improve safety, compliance, and user confidence.

Build the Chat UI and Interaction Patterns

A good LLM chat experience is mostly UX. If the interface is unclear or feels slow, users won’t trust the answers—even when the model is right.

Core chat UI: make the basics unmistakable

Start with a simple layout: a clear input box, a visible send button, and messages that are easy to scan.

Include message states so users always know what’s happening:

• Sending… (message is on the way)
• Streaming… (assistant is typing)
• Done (final answer)
• Failed (needs retry)

Add timestamps (at least per message group) and subtle separators for long conversations. This helps users return later and understand what changed.

Streaming responses: speed users can feel

Even if total generation time is the same, streaming tokens makes the app feel faster. Show a typing indicator immediately, then stream the response as it arrives. If you also support “Stop generating,” users feel in control—especially when the answer goes off track.

Helpful patterns: guide people without getting in the way

Many users don’t know what to ask. A few lightweight helpers can increase successful sessions:

• Suggested prompts under the input (e.g., “Summarize this,” “Draft a reply,” “Find action items”)
• Quick actions on messages (Copy, Regenerate, Shorter, More detail)
• File upload when your use case benefits from documents—show upload progress, and confirm what was received (filename, size, pages)

Error handling: graceful, not scary

Design for failures up front: network drops, rate limits, and tool errors will happen.

Use friendly, specific messages (“Connection lost. Retry?”), offer one-click retry, and keep the user’s draft text. For long requests, set clear timeouts, then provide a “Try again” state with options: retry, edit prompt, or start a new thread.

Safety, Security, and Policy Controls

If your app can chat, it can also be tricked, stressed, or misused. Treat safety and security as product requirements, not “nice to have.” The goal is simple: prevent harmful outputs, protect user and company data, and keep the system stable under abuse.

Policy checks for risky requests

Define what your app should refuse, what it can answer with constraints, and what requires a handoff. Common categories: self-harm, medical/legal/financial advice, hate/harassment, sexual content (especially involving minors), and requests to generate malware or evade security.

Implement a lightweight moderation step before (and sometimes after) generation. For sensitive topics, switch to a safer response mode: provide high-level information, encourage professional help, and avoid step-by-step instructions.

Reduce prompt injection and data leakage

Assume retrieved documents and user messages may contain malicious instructions. Keep a strict separation between:

• System instructions (your non-negotiable rules)
• Tool output / retrieved content (treated as untrusted evidence)
• User requests

In practice: clearly label retrieved passages as reference text, never merge them into the instruction layer, and only allow the model to use them to answer the question. Also, redact secrets from logs and never place API keys in prompts.

Abuse prevention: auth, limits, and monitoring

Require authentication for anything that touches private data or paid resources. Add rate limits per user/IP, anomaly detection for scraping patterns, and hard caps on tool calls to prevent runaway costs.

User reporting and human escalation

Add a visible “Report answer” button in the chat UI. Route reports to a review queue, attach conversation context (with PII minimized), and provide an escalation path to a human operator for high-risk cases or repeated policy violations.

Test and Evaluate Before You Ship

You can’t eyeball an LLM chat experience and hope it will hold up once real users arrive. Before launch, treat evaluation like a product quality gate: define what “good” looks like, measure it repeatedly, and block releases that regress.

Build a realistic test set

Start by creating a small but representative test set of conversations. Include typical happy paths, messy user messages, ambiguous requests, and edge cases (unsupported features, missing data, policy-violating prompts). Add expected outcomes for each: the ideal answer, what sources should be cited (if using RAG), and when the assistant should refuse.

Measure quality with clear signals

Track a few core metrics that map to user trust:

• Accuracy: Does it answer correctly for the scenario?
• Groundedness: Are claims supported by your retrieved data, or is it guessing?
• Refusal correctness: When a request should be declined, does it refuse clearly and safely—without being overly strict?

Even a simple reviewer rubric (1–5 scores + a short “why”) will outperform informal feedback.

Validate tool calls end-to-end

If your bot takes actions, test tool calls as carefully as API endpoints:

• Verify it sends correct parameters (types, required fields, units).
• Exercise retries and partial failures.
• Enforce idempotency so repeated calls don’t duplicate orders, tickets, or messages.

Log tool inputs/outputs in a way you can audit later.

Run controlled experiments

Use A/B tests for prompt and UI changes rather than shipping guesses. Compare variants on your fixed test set first, then (if safe) in production with a small traffic slice. Tie outcomes to business success metrics (task completion, time-to-resolution, escalation rate), not just “it sounds nicer.”

Manage Cost, Latency, and Reliability

A chat experience can feel “free” during a prototype and then surprise you in production—either with a big bill, slow responses, or intermittent failures. Treat cost, speed, and uptime as product requirements, not afterthoughts.

Predict and control spend

Start by estimating token usage per chat: average user message length, how much context you send, typical output length, and how often you call tools or retrieval. Multiply by expected daily chats to get a baseline, then set budget alerts and hard limits so a runaway integration can’t drain your account.

A practical trick is to cap the expensive parts first:

• Max context size (don’t always send the full conversation)
• Max response length (users usually prefer concise answers anyway)
• Max tool calls per turn (avoid loops and tool spam)

Reduce latency without hurting quality

Most latency comes from (1) model time and (2) waiting on tools/data sources. You can often cut both:

• Apply caching for common questions (e.g., “pricing,” “reset password”) and repeated retrieval results. Cache should key on normalized user intent + relevant user segment, not raw text alone.
• Parallelize what you can: run retrieval and lightweight checks at the same time, then compose the final answer.
• Keep prompts lean. Extra instructions and long histories increase tokens and response time.

Use model routing

Not every message needs your biggest model. Use routing rules (or a small classifier) so a smaller, cheaper model handles straightforward tasks (FAQs, formatting, simple extraction) and a larger model handles complex reasoning, multi-step planning, or sensitive conversations. This usually improves both cost and speed.

Engineer reliability like a real service

LLMs and tool calls will fail sometimes. Plan for it:

• Timeouts and retries with backoff for tool requests
• Fallbacks (alternate model, simpler answer, or “try again” UX)
• Circuit breakers when a dependency is unstable
• Clear partial failure responses (“I couldn’t reach your calendar—want me to retry?”)

Done well, users experience a fast, steady assistant—and you get predictable costs you can scale.

Deploy, Monitor, and Improve Over Time

Shipping your LLM chat experience is the start of the real work. Once users interact with it at scale, you’ll discover new failure modes, new costs, and new opportunities to make the assistant feel smarter by tightening prompts and improving retrieval content.

Monitor what users feel (and what breaks)

Set up monitoring that connects technical signals to user experience. At minimum, track latency (p50/p95), error rates, and distinct failure categories—model timeouts, tool/function-call failures, retrieval misses, and UI delivery issues.

A useful pattern is to emit one structured event per message with fields like: model name/version, token counts, tool calls (name + status), retrieval stats (docs returned, scores), and user-visible outcome (success/abandon/escalation).

Log prompts and outputs safely

You’ll want examples to debug and improve—but store them responsibly. Log prompts and model outputs with automated redaction for sensitive fields (emails, phone numbers, addresses, payment details, access tokens). Keep raw text access limited, time-bound, and audited.

If you need to replay conversations for evaluation, store a sanitized transcript plus a separate encrypted blob for any sensitive content, so most workflows never touch the raw data.

Build a tight feedback loop

Add a lightweight feedback control in the UI (thumbs up/down + optional comment). Route negative feedback into a review queue with:

• the sanitized transcript
• the retrieved passages (if using RAG)
• tool call traces and errors

Then act on it: adjust prompt instructions, add missing knowledge to your retrieval sources, and create targeted tests so the same issue can’t regress quietly.

Communicate change: roadmap and expectations

LLM behavior evolves. Publish a clear roadmap so users know what’s improving next (accuracy, supported actions, languages, integrations). If features differ by plan—like higher rate limits, longer history, or premium models—point users to /pricing for plan details and keep those limits explicit inside the product UI.

If your goal is to ship quickly while keeping an option to “graduate” to a fully custom stack later, consider building an initial version on Koder.ai (with source code export and snapshots/rollback), then harden it with your evaluation, safety, and observability practices as usage grows.