Chat-first приложения: как проектировать диалоги, которые любят пользователи

Introduction

The term "chat-first" describes applications where the dialog interface is the central way for the user to interact with the system. Unlike classic applications with menus and forms, chat-first applications are focused on natural speech, quick contextual access to functions, and multitasking. This approach has been made possible by advances in natural language processing and the availability of large language models.

Why does the "chat-first" approach appear?

The transition to conversational interfaces is stimulated by several factors: a lower entry barrier for users, more flexible task routing, and the ability to combine multiple services in one dialog. Users are used to instant messengers, and turning the app into a similar experience helps improve retention and learning rate.

In addition, LLMs allow you to process free text, extract intentions, and apply complex logic without explicitly designing multiple forms and steps. This saves the product time and makes the interface more adaptive.

User Experience (UX) and dialog design

The UX for chat-first applications requires a revision of familiar patterns. It is important to design conversations based on the user's goals: short subgoals, explicit options, and the ability to quickly cancel or refine an action. Designers should think not only about the appearance, but also about the voice, tone, and expectations of the system's response.

• Transparency:
• Contextuality:
• Controllability:

Architecture and key components

The typical architecture of a chat-first application includes: a chat interface, a language processing layer (NLU), a dialog orchestrator, business logic, and integration with external services. Each layer must be modular so that components can be replaced (for example, different NLU models).

Consider the following components:

1. Gateway: API for receiving messages and authentication.
2. Message broker: queues and subscriptions for scaling.
3. NLU/LLM: Intent recognition, entity extraction, and response generation.
4. Dialog Orchestrator: logic for managing dialog states.
5. Integrations: CRM, databases, external APIs.

LLM integration and context management

LLM is a powerful tool, but it needs to be used carefully. Context management includes reducing and prioritizing messages, creating system prompts, and using auxiliary prompts to limit the behavior of the model.

Practical advice:

• Keep the key context separate and substitute it as needed.
• Use semantic search to access relevant information under the constraints of the model context.
• Limit the generation of actions and orders — confirm critical operations.

Data, privacy and security

In chat-first applications, the exchange of information in free form increases security requirements. It is necessary to encrypt messages, introduce access control and political filters at the data level. Special attention is paid to personal data and sensitive information.

Recommendations:

• Minimize the transfer of personal data to external LLM calls without anonymization.
• Log interactions, but apply data masking.
• Implement the ability to delete history at the user's request.

Design patterns and best practices

Useful patterns include slot-based dialogues for information gathering, multi-agent architectures for role allocation (search, order processing, consultations), and the use of action pipelines.

A few more practical techniques:

1. Separate "understanding" and "action": the model parses the intent, and a separate module performs changes to the data.
2. Write down fallback scenarios and safe responses for cases of uncertainty.
3. Maintain a hybrid interface: chat + quick buttons/forms to speed up routine tasks.

Testing, monitoring and analytics

Testing dialog systems is different from testing traditional UI. It is necessary to cover scenarios with variable input, test NLU for inaccuracy tolerance, and track response quality metrics.

Key metrics:

• Intent Accuracy
• Percentage of successful completion of tasks (Task Completion Rate)
• Average dialog time and user satisfaction

Scaling and operation

To scale a chat-first application, it is important to properly queue messages, cache the context, and manage the frequency of calls to the LLM. It also makes sense to use asynchronous processes for heavy computing and deferred tasks.

During operation, take into account model updates and rollback of changes: versioning hints and configurations, conduct A/B testing of model behavior and monitor performance deviations.

Business models and examples

Chat-first applications are applicable in support services, internal assistants, educational products, and personalized marketing. Business models include subscriptions, pay-per-request, premium features, and integration with payment systems.

Example: A customer support company reduces the average request processing time by 40% after implementing a chat-first assistant that automatically collects data and redirects complex cases to specialists.

Conclusion and further steps

Chat-first applications open up new possibilities for interaction and automation. The key to successful implementation is a thoughtful architecture, a responsible approach to data, and careful dialog design. Start with a prototype, identify critical user scenarios, and gradually implement LLM as an assistant rather than the sole source of logic.

Next, it's worth exploring integrations with specific models, testing several hint options, and building monitoring and rollback processes for secure output to production. This approach will ensure a balance between innovation and manageable risk.

Laravel/PHP/Laragent.io development

Laravel remains one of the most convenient frameworks for developing chat-first applications in PHP due to its expressive architecture, rich ecosystem set, and built-in tools for queuing tasks and events. For applications with a dialog interface, it is useful to use the mechanisms of Broadcasting and WebSockets (Laravel Echo, Pusher or Swoole) for instant delivery of messages and status updates of the dialog.

A specific approach is to put the logic of dialog orchestration into a separate service or package (for example, Laragent.io as an example of a platform for creating agents). Such a package can manage prompts, versioning of system prompts, and routing of user intents to specific handlers. Middlewares for validating incoming messages, masking personal data before sending it to third-party LLMs, and integration with queues for deferred or heavy computing are also important.

• Use Laravel Queues (Redis, SQS) for asynchronous processing of LLM requests and heavy tasks.
• Broadcasting + WebSockets for real-time updates and user feedback.
• Separate NLU/intent parsing from business logic: delegate processing to separate services/agents.
• Versioning hints and configurations in the repository, provide the ability to rollback.

When deploying, pay attention to the scale: horizontal scaling of queue workers and the stability of the WebSocket service, monitoring the consumption of LLM tokens and limiting the frequency of requests. In addition, set up logging and masking processes to facilitate auditing and compliance with data security requirements.

Python development

Python offers flexibility and a set of libraries that make it a natural choice for prototyping and production services with LLM. Modern stack options include FastAPI for building asynchronous processing APIs, uvicorn/Hypercorn for the server, and Celery or RQ for background tasks. To work with LLM and context management, wrappers and frameworks like LangChain are often used, which help build call chains, semantic search, and hint templates.

Architecturally, it makes sense to separate the web layer and the agent layer./orchestration and backend for storing context (Postgres, Redis) and vector index (FAISS, Milvus). For real-time interaction, use WebSocket endpoints (for example, built into FastAPI) or asynchronous message queues. Don't forget about testing: write unit and integration tests for intent handlers and rollback scenarios.

• LangChain, LlamaIndex, and other libraries simplify the construction of contextual pipelines.
• Use asynchronous calls to the LLM and connection pooling to avoid blockages.
• Keep vector representations separate and update the index as data accumulates.
• Automate deployment via containers (Docker) and orchestration (Kubernetes) for scalability.

The Python ecosystem also makes it easier to experiment with new models and quickly replace components: thanks to the modular structure of the services, you can test multiple LLM providers, change your context management strategy, and integrate additional analytics tools without completely redesigning the application.