DEV Community: HyunKi Lee

Refining AI Output: A Guide for Clearer Mobile App Specs

HyunKi Lee — Sun, 23 Aug 2026 08:46:02 +0000

Refining AI Output for Cleaner Dev Specs: A Systems Approach to Executable Mobile Requirements

When you ask a large language model to generate a mobile app specification, the initial output is almost always high-entropy text. It looks plausible at a glance, but it lacks the structural rigor required for actual engineering. A prompt requesting a "mobile checkout flow" typically returns a bulleted list of UI steps, completely omitting critical details like offline state handling, race conditions during payment tokenization, or strict validation schemas for the payload.

To transform this raw output into an executable specification, we must treat the generation process as a multi-pass compilation pipeline. This article explores the systematic methodology of refining AI output, moving from ambiguous natural language to deterministic, implementation-ready specifications.

The Problem of Unconstrained Generation

The core issue with raw generative output is not a lack of capability in the underlying models, but rather the absence of constraints. In system design, unconstrained generation leads to drift. If a developer attempts to write code directly from a first-draft specification generated by a model, they inevitably encounter gaps that require real-time, undocumented design decisions. This introduces architectural debt before the first line of code is even committed.

A well-structured plan is not a prelude to the work; it is the high-leverage phase of the work itself. By narrowing the decision space early, we identify critical integration factors and resolve ambiguities before committing engineering resources. To achieve this, we must implement a structured refinement process that subjects the initial output to adversarial validation.

The Multi-Pass Refinement Pipeline

Instead of relying on a single prompt to generate a complete specification, we structure the refinement into three distinct phases:

Structural Schema Definition: Defining the exact data models and state boundaries.
State Transition Mapping: Explicitly declaring how the system moves between states, including failure modes.
User Story Synthesis: Generating developer-readable requirements mapped directly to the validated schema and state machine.

Here is a pseudo-code representation of how this refinement pipeline is structured programmatically within a system like Bridge:

interface RawSpec {
  description: string;
}

interface RefinedSpec {
  schema: object;
  states: string[];
  transitions: Array<{ from: string; to: string; trigger: string }>;
  userStories: string[];
}

async function refineSpecification(raw: RawSpec): Promise<RefinedSpec> {
  // Phase 1: Extract and validate the data schema
  const schema = await extractSchema(raw.description);

  // Phase 2: Map the state machine and identify missing transitions
  const stateMachine = await mapStateTransitions(raw.description, schema);

  // Phase 3: Generate structured user stories bound to the schema and states
  const userStories = await generateUserStories(schema, stateMachine);

  return {
    schema,
    states: stateMachine.states,
    transitions: stateMachine.transitions,
    userStories
  };
}

Phase 1: Enforcing Structural Schemas

A common failure mode in raw specifications is the use of vague data descriptions. For example, a system might state: "The system sends the user's payment details to the backend." This is insufficient. We need to define the exact payload structure.

By passing the raw output through a schema extraction step, we force the system to output a strict JSON Schema or TypeScript interface. If the system cannot resolve a field (for example, whether the billing address is required or optional), the refinement pipeline flags this as an ambiguity for human review.

Here is a concrete, runnable TypeScript example of the schema we expect the refinement process to produce for our checkout flow:

export interface CheckoutPayload {
  cartId: string;
  paymentMethod: {
    type: 'credit_card' | 'digital_wallet';
    token: string;
  };
  shippingAddress: {
    street: string;
    city: string;
    postalCode: string;
    country: string;
  };
  billingAddressSameAsShipping: boolean;
  billingAddress?: {
    street: string;
    city: string;
    postalCode: string;
    country: string;
  };
}

If the initial output omitted the conditional logic for the billing address, the validation step detects the missing relationship and prompts the generator to resolve it.

Phase 2: Mapping State Transitions

Mobile applications are highly state-dependent. Network latency, backgrounding, and user interruptions mean that a screen is rarely in a simple loading or loaded state.

A raw specification often assumes a happy path: Idle to Loading to Success. A refined specification must account for the complete state space. We can represent this as a state transition matrix. Let us look at a simplified state machine definition for our checkout process:

type CheckoutState = 
  | 'Idle'
  | 'ValidatingCart'
  | 'TokenizingPayment'
  | 'ProcessingTransaction'
  | 'Success'
  | 'Error_Network'
  | 'Error_Declined';

interface Transition {
  current: CheckoutState;
  action: 'SUBMIT' | 'RETRY' | 'CANCEL' | 'RESOLVE' | 'FAIL';
  next: CheckoutState;
}

const checkoutTransitions: Transition[] = [
  { current: 'Idle', action: 'SUBMIT', next: 'ValidatingCart' },
  { current: 'ValidatingCart', action: 'RESOLVE', next: 'TokenizingPayment' },
  { current: 'ValidatingCart', action: 'FAIL', next: 'Idle' },
  { current: 'TokenizingPayment', action: 'RESOLVE', next: 'ProcessingTransaction' },
  { current: 'TokenizingPayment', action: 'FAIL', next: 'Error_Network' },
  { current: 'ProcessingTransaction', action: 'RESOLVE', next: 'Success' },
  { current: 'ProcessingTransaction', action: 'FAIL', next: 'Error_Declined' },
  { current: 'Error_Network', action: 'RETRY', next: 'TokenizingPayment' },
  { current: 'Error_Declined', action: 'CANCEL', next: 'Idle' }
];

By forcing the system to map every state to a corresponding transition, we expose gaps in the initial product concept. For instance, what happens if the user experiences a network error during the transaction processing phase? The state machine forces us to decide whether we allow a retry (which risks double-charging without idempotency keys) or route the user to a support flow.

Phase 3: Synthesizing Executable User Stories

Once the data schema and state transitions are locked down, we can synthesize user stories that are actually useful to a developer. Instead of generic descriptions, these stories reference the specific states and schemas defined in the previous steps.

A refined user story looks like this:

"As a user in the Error_Network state, when I trigger the RETRY action, the system must attempt to re-tokenize the payment using the existing CheckoutPayload without forcing me to re-enter my shipping address."

This level of precision eliminates guesswork during the implementation phase.

Trade-offs of Strict Refinement

Implementing a multi-pass refinement pipeline is not without trade-offs. It requires more computational overhead and initial setup than simply asking a model to write a document. It also introduces friction during the early ideation phase, as the system will repeatedly flag incomplete ideas as validation errors.

However, this friction is intentional. It is far cheaper to resolve a state machine conflict or a missing data field during the planning phase than it is to refactor a React Native or Swift codebase mid-sprint. By narrowing the decision space early, we ensure that when a developer begins writing code, they are executing against a stable, validated blueprint.

Conclusion

Refining AI output is not a matter of finding the perfect prompt. It is about building a structured, multi-pass system that subjects raw generation to strict validation schemas and state machine constraints. By treating planning as a phase of execution, we turn ambiguous concepts into precise, developer-ready specifications.

AI Software Architecture: Turn Taste into Mobile Specs

HyunKi Lee — Thu, 20 Aug 2026 00:14:13 +0000

Translating Developer Taste into Specs: A Systems Approach to AI Software Architecture

When building a mobile application, the initial bottleneck is rarely writing the code. The bottleneck is the translation layer: converting a high-level product intuition into a deterministic system design. A developer knows when an interaction feels correct, but translating that taste into a formal schema, a state machine, and a set of API contracts requires hours of manual translation.

Traditional software engineering relies on Product Requirement Documents (PRDs) and Requests for Comments (RFCs) to bridge this gap. However, these documents are often written in natural language, which is inherently ambiguous. When we attempt to use automated systems to generate code from ambiguous natural language, the output is fragile. To build robust systems, we must treat AI software architecture as a structured compilation process. The goal is to compile high-level developer intent into a strict, machine-readable specification before a single line of application code is written.

The Architecture of Intent Translation

To translate developer taste into executable specifications, we must establish a formal pipeline. We cannot rely on a single prompt to generate an entire application. Instead, we decompose the process into discrete, deterministic phases. This approach aligns with the core belief that planning is execution: by narrowing the decision space early, we identify critical factors and commit resources only after the plan survives adversarial review.

The pipeline consists of four distinct phases:

Domain Schema Extraction: Defining the core entities, their attributes, and their relationships.
State Transition Mapping: Defining the valid states of the application and the transitions between them.
Interface Contract Generation: Defining the API endpoints or local database queries required to support the state transitions.
Adversarial Validation: Testing the generated specification against edge cases and race conditions before code generation begins.

By separating these concerns, we ensure that each phase has a narrow, well-defined responsibility. This makes the system predictable and allows developers to inspect and modify the intermediate outputs at each stage.

Implementing the Specification Pipeline

Let us examine how to implement this pipeline using a structured approach. We can define a system that takes a raw product description and outputs a validated JSON schema representing the application state.

Below is a pseudo-code implementation of the specification generator. This script demonstrates how to orchestrate the planner to extract a domain schema and validate it against structural constraints.

# Pseudo-code: Structured specification pipeline for domain schemas
import json
from typing import Dict, Any

class SchemaValidationError(Exception):
    pass

class SpecGenerator:
    def __init__(self, planner_client):
        self.planner = planner_client

    def generate_domain_schema(self, raw_intent: str) -> Dict[str, Any]:
        prompt = (
            "Analyze the following product intent and extract the core data schema. "
            "Output a valid JSON Schema containing entities, fields, types, and relationships. "
            f"Intent: {raw_intent}"
        )
        raw_response = self.planner.complete(prompt, response_format="json")
        try:
            schema = json.loads(raw_response)
            self._validate_schema_structure(schema)
            return schema
        except (json.JSONDecodeError, SchemaValidationError) as e:
            # Fallback or retry logic would be implemented here
            raise RuntimeError(f"Failed to generate valid schema: {e}")

    def _validate_schema_structure(self, schema: Dict[str, Any]) -> None:
        required_keys = ["entities", "relationships"]
        for key in required_keys:
            if key not in schema:
                raise SchemaValidationError(f"Missing required key: {key}")

        for entity in schema["entities"]:
            if "name" not in entity or "fields" not in entity:
                raise SchemaValidationError("Entities must contain 'name' and 'fields'")

This pseudo-code illustrates the first phase of the pipeline. By enforcing a strict JSON schema output and validating the structure programmatically, we eliminate the ambiguity of natural language. The output of this phase becomes the input for the next phase: generating the state transition machine.

Mapping State Transitions

Once the domain schema is established, we must define how the application transitions between states. For example, if we are building a mobile task manager, a task entity might transition from "Pending" to "In Progress" to "Completed".

Instead of letting the code generator guess these transitions, we explicitly map them using a finite state machine (FSM) specification. This prevents invalid state transitions, such as a task moving directly from "Pending" to "Completed" without passing through "In Progress" if the business logic forbids it.

Here is an example of a generated state machine specification in JSON format:

{
  "states": ["Pending", "InProgress", "Completed", "Archived"],
  "initial_state": "Pending",
  "transitions": [
    {
      "from": "Pending",
      "to": "InProgress",
      "trigger": "START_TASK"
    },
    {
      "from": "InProgress",
      "to": "Completed",
      "trigger": "COMPLETE_TASK"
    },
    {
      "from": "Completed",
      "to": "Archived",
      "trigger": "ARCHIVE_TASK"
    },
    {
      "from": "Pending",
      "to": "Archived",
      "trigger": "ARCHIVE_TASK"
    }
  ]
}

By generating this specification first, the developer can review the state transitions and verify that they match the intended product behavior. If a transition is missing or incorrect, the developer can modify the specification directly before any UI code is generated.

Adversarial Validation and Trade-offs

A key component of AI software architecture is adversarial validation. Once the system generates the domain schema and the state machine, we run a validation step where a separate instance of the planner acts as an adversary. The adversary's role is to identify edge cases, security vulnerabilities, or logical inconsistencies in the specification.

For instance, the adversary might ask: "What happens if a user attempts to archive a task that is currently in progress?" or "Does the schema support offline synchronization if the network connection is lost?"

This process highlights several trade-offs:

Rigor versus Velocity: Defining strict schemas and state machines requires more upfront planning. However, this investment reduces the time spent debugging runtime errors and refactoring poorly structured code later in the development cycle.
Determinism versus Flexibility: By constraining the planner to output structured JSON schemas, we limit its creative freedom. This is a deliberate choice: in software architecture, predictability is far more valuable than novelty.
Tooling Complexity: Implementing a multi-stage pipeline with validation steps requires more complex infrastructure than a simple single-prompt code generator. The benefit is a system that produces reliable, production-grade code.

Conclusion

Translating developer taste into concrete specifications is not about automating away the developer. It is about using structured systems to handle the tedious work of drafting schemas, state machines, and API contracts, allowing developers to focus on high-level design and user experience. By treating AI software architecture as a compilation process, we can turn abstract product intuition into a deterministic, executable development plan.

From a Shower Thought to a Database Schema in Four Steps

HyunKi Lee — Sat, 16 May 2026 07:39:21 +0000

Bridge is launching soon — a platform for moving mobile app ideas through architecture and into shipped products. This post is a preview of the kind of thinking we'll publish once the blog goes live: a 4-step AI-assisted workflow for turning a "shower thought" into a validated database schema, with human review kept at the center. If you want early access to the launch and future posts, sign up at https://bridgedev.io.