Banking & Fintech Prompt Engineering Library 🦀🏦

A simplified, educational Rust library for learning prompt engineering concepts and Rust programming patterns, specialized for banking and financial services applications.

🎯 Learning Objectives

This library is designed to teach:

• Builder Pattern: Fluent API design for constructing complex objects
• Trait-based Abstraction: Creating interfaces for different implementations
• Async Programming: Non-blocking operations with async/await
• Structured Code: Clean, readable, and well-documented Rust code
• Prompt Engineering: Organizing and structuring prompts for LLMs

🚀 Quick Start

Run the Demo

cargo run

Run Tests

cargo test

📚 Core Concepts

1. Prompt Structure

Prompts are organized into logical sections:

• Goal: What you want to achieve
• Context: Background information
• Role: The persona for the LLM to adopt
• Steps: Specific instructions or actions
• Examples: Input/output demonstrations
• Output: Desired response format

2. Builder Pattern

let prompt = PromptBuilder::new()
    .goal("Analyze customer feedback")
    .role("Data analyst")
    .step("Load the data")
    .step("Perform analysis")
    .output("Summary report")
    .build();

3. Template System

Pre-built templates for banking and fintech use cases:

let template = PromptTemplate::CreditRiskAssessment {
    credit_type: "personal loans".to_string(),
    risk_focus: "default probability assessment".to_string(),
};
let prompt = template.to_builder().build();

4. Available Banking & Fintech Templates

• CreditRiskAssessment: For loan evaluation and credit risk analysis
• FraudDetection: For transaction monitoring and fraud prevention
• RegulatoryCompliance: For compliance assessment and audit preparation
• MarketAnalysis: For financial market analysis and investment insights
• FinancialAdvisory: For wealth management and investment recommendations

5. LLM Client Abstraction

#[async_trait]
pub trait SimpleLLMClient: Send + Sync {
    async fn generate(&self, prompt: &str) -> Result<String>;
}

Crafting High-Performance LLM Agents with ThoughtStage-Style Prompts

A synthesis of the latest research & concrete engineering advice

---

1 | Why Structured Prompts Matter

Large-language-model agents don't just need words—they need a scaffold that makes their reasoning explicit, constrains their output and bakes in safety. Our internal ThoughtStage enum captures that scaffold:

enum ThoughtStage {
    Goal(StageInput),
    Context(StageInput),
    Assumption(StageInput),
    Step(StageInput),
    Role(StageInput),
    Output(StageInput),
    TokenLimit(StageInput),
    WordLimit(StageInput),
    Reasoning(StageInput),
    Guardrail(StageInput),
    Rubric(StageInput),
    Example { user: StageInput, assistant: StageInput },
    Style(StageInput),
    Meta(StageInput),
}

Recent literature shows that systems which approximate this structure consistently outperform ad-hoc prompting. Below we merge four key papers—and one especially relevant survey—into practical guidance for each stage.

---

2 | The Core Survey: Towards Goal-oriented LLM Prompting

Li et al.'s survey (Jan 2024) proposes five iterative stages—Goal Decomposition → Action Selection → Action Execution → Sub-goal Evaluation → Valuable-Sub-goal Selection—and reviews 50 studies that implement them. Performance gains are substantial:

Technique	Task	Reported Boost
Chain-of-Thought (CoT)	arithmetic reasoning	+22.6 pp accuracy
Self-consistency	arithmetic reasoning	+32.5 pp accuracy
Self-refine	code Q&A	+21.1 pp F1
SayPlan	embodied planning	+73 pp "excitability" over baseline planner

Those five survey stages map neatly onto ThoughtStage:

Survey Stage	ThoughtStage analogue
Goal Decomposition	Goal, Step
Action Selection	Role, Assumption
Action Execution	(implicit in LLM call)
Sub-goal Evaluation	Reasoning, Guardrail
Valuable-Sub-goal Selection	Rubric, Example

(arXiv)

---

3 | Complementary Findings & How They Map

Paper	Core Idea	Relevant ThoughtStages
Tree of Thoughts (Yao et al., May 2023)	Explore many reasoning paths; backtrack if needed → +70 pp on Game-of-24	Step, Reasoning, Rubric (select best path) (arXiv)
Prompt Pattern Catalog (White et al., Feb 2023)	16 reusable patterns (Persona, Template…)	Role, Output, Style, Meta (arXiv)
Prompt Canvas (Hewing & Leinhos, 2024)	Worksheet with Goal, Context, Role, Style, Guardrails, Examples	Directly mirrors most enum fields
Content–Format-Integrated Prompt Optimisation (CFPO) (Liu et al., 2025)	Jointly tune wording and formatting → +3-7 pp on reasoning tasks	Output, TokenLimit, Style, Meta
Prompt Formatting Impact Study (He et al., 2024)	Markdown sections & bullet lists swing accuracy by up to 40 %	Output, Style, TokenLimit
Efficient Prompting Survey (Chang et al., Apr 2024)	Prompt compression & length caps cut latency/$$	TokenLimit, WordLimit (arXiv)

---

4 | Stage-by-Stage Best-Practice Cheatsheet

ThoughtStage	What the papers recommend
Goal	State desired end-state in ≤ 20 words (Li et al.).
Context	Separate paragraph labelled Context: (Prompt Canvas).
Assumption	Bullet-list hidden premises; keep each under 80 chars (CFPO).
Step	Numbered sub-tasks 1., 2., 3.; pairs well with Tree-of-Thought sampling.
Role	"You are a {domain-expert} …" boosts BLEU +6 % on translation (Canvas).
Output	Specify format + tone (e.g. "Markdown table, concise").
Token/Word Limits	Always add a hard cap—smaller models otherwise over-run (He et al.).
Reasoning	Enable CoT or ToT only for models ≥ GPT-4 quality; else skip to save tokens.
Guardrail	Use a negative→positive pattern: "Do not reveal keys. Instead…"
Rubric	Put acceptance criteria after guardrails; self-check loops use it.
Example	One or two few-shot pairs still yield +12 pp EM on QA (CFPO data).
Style	Limit to one adjective pair ("concise & persuasive"); more dilutes adherence.
Meta	End with parsing instructions ("Return JSON only, no commentary").

---

5 | Putting It All Together

Below is a minimal but research-backed prompt template (Markdown). Swap [ … ] for your content and you have an agent-ready script fully aligned with the literature.

## Goal
[Write a weekly engineering digest]

## Context
[Audience: Staff engineers & managers in fintech]

## Assumptions
- Readers understand basic security jargon
- No confidential data may be disclosed

## Steps
1. Summarise top merged PRs
2. Highlight risk items
3. Provide next-week plan

## Role
You are the company's Senior Staff Engineer.

## Guardrails
Do **not** reveal internal repository URLs. Instead, refer to modules abstractly.

## Rubric
✓ Covers at least 3 PRs  
✓ Includes a bullet-point action list  
✓ ≤ 600 tokens

## Example
**User:** What is least-privilege?  
**Assistant:** Least-privilege means each identity has only the permissions essential to perform its tasks.

## Output
Markdown with h2 sections; TL;DR at top.

## Style
Concise & authoritative.

## Meta
Return Markdown only, no extra commentary outside sections.

---

6 | Limitations & Future Work

• Guardrail + Rubric research is thin. Current papers treat them as sub-steps; explicit stage-wise studies are needed.
• Efficiency trade-offs. Compression methods (Chang et al.) are promising but can harm interpretability—ideal for background tasks, not user-visible answers.
• Hierarchical prompts. Li et al. flag multi-level decomposition (scripts, planning) as the next frontier—perfect for extending Meta or adding new enum variants.

---

7 | Take-home for builders

1. Adopt the full ThoughtStage schema—it subsumes best practices across all surveyed work.
2. Log A/B experiments on format as vigorously as on content—40 % swings are common.
3. Automate evaluation: feed your Rubric back through self-critique loops (e.g., SelfCheck, CRITIC).
4. Cache + reuse prompts—efficient prompting cuts both tokens and latency.

---

8 | Key References

1. Li H. et al., "Towards Goal-oriented Prompt Engineering for Large Language Models: A Survey." arXiv, 2024. (arXiv)
2. Yao S. et al., "Tree of Thoughts: Deliberate Problem Solving with LLMs." arXiv, 2023. (arXiv)
3. White J. et al., "A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT." arXiv, 2023. (arXiv)
4. Liu Y. et al., "Content-Format Integrated Prompt Optimisation (CFPO)." arXiv, 2025.
5. He H. et al., "Does Prompt Formatting Have Any Impact on LLM Performance?" arXiv, 2024.
6. Chang K. et al., "Efficient Prompting Methods for Large Language Models: A Survey." arXiv, 2024. (arXiv)

Use this blueprint as your next blog post—or the backbone of a talk—and equip your agents with a research-grade prompt architecture. Happy building!

Crafting High-Performance LLM Agents with ThoughtStage-Style Prompts

A synthesis of the latest research & concrete engineering advice

---

1 | Why Structured Prompts Matter

Large-language-model agents don't just need words—they need a scaffold that makes their reasoning explicit, constrains their output and bakes in safety.

8 | Key References

1. Li H. et al., "Towards Goal-oriented Prompt Engineering for Large Language Models: A Survey." arXiv, 2024.
2. Yao S. et al., "Tree of Thoughts: Deliberate Problem Solving with LLMs." arXiv, 2023.
3. White J. et al., "A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT." arXiv, 2023.

Use this blueprint as your next blog post—or the backbone of a talk—and equip your agents with a research-grade prompt architecture. Happy building!

🏗️ Architecture

The library consists of:

1. Core Data Structures (PromptSection, Prompt)
2. Builder Pattern (PromptBuilder)
3. Template System (PromptTemplate with 5 common patterns)
4. LLM Interface (SimpleLLMClient trait)
5. Mock Implementation (MockLLMClient)
6. Demo & Tests

🔍 Code Walkthrough

Main Components

// Define what goes in a prompt
pub enum PromptSection {
    Goal(String),
    Context(String),
    Role(String),
    Step(String),
    Example { user: String, assistant: String },
    Output(String),
}

// Build prompts fluently
pub struct PromptBuilder {
    prompt: Prompt,
}

// Abstract LLM communication
#[async_trait]
pub trait SimpleLLMClient: Send + Sync {
    async fn generate(&self, prompt: &str) -> Result<String>;
}

Usage Examples

Manual Prompt Building

// Build a structured credit risk assessment prompt manually
let prompt = PromptBuilder::new()
    .goal("Assess credit risk for mortgage application")
    .context("30-year fixed rate mortgage, $400k loan amount")
    .role("Senior Credit Risk Analyst")
    .step("Analyze credit history and FICO score")
    .step("Evaluate debt-to-income ratio")
    .step("Assess employment stability")
    .example(
        "FICO 720, DTI 35%, 3 years employment",
        "Moderate risk - approve with standard terms"
    )
    .output("Risk assessment with approval recommendation")
    .build();

Template-Based Building

// Use a pre-built template for fraud detection
let template = PromptTemplate::FraudDetection {
    channel: "online banking".to_string(),
    scope: "real-time monitoring".to_string(),
};
let prompt = template.to_builder().build();

// Or customize a template for regulatory compliance
let compliance_template = PromptTemplate::RegulatoryCompliance {
    regulation: "AML/KYC".to_string(),
    focus: "customer due diligence".to_string(),
};
let custom_prompt = compliance_template.to_builder()
    .step("Review beneficial ownership documentation")
    .build();

// Send to LLM
let llm_client = MockLLMClient;
let response = llm_client.generate(&prompt.to_string()).await?;

🧪 Testing

The library includes comprehensive tests demonstrating:

• Builder pattern functionality
• Mock LLM client behavior
• Example section formatting
• Error handling

🎓 Educational Value

This simplified version focuses on:

✅ Readability: Every line is documented and explained
✅ Simplicity: Core concepts without overwhelming complexity
✅ Best Practices: Rust patterns and async programming
✅ Practical Examples: Real-world prompt engineering scenarios
✅ Progressive Learning: Build understanding step by step

🛠️ Dependencies

Minimal dependencies for educational clarity:

• anyhow: Error handling
• async-trait: Async trait support
• serde: Serialization (for structured data)
• tokio: Async runtime

🚀 Next Steps

After understanding this simple version, you can:

1. Add real LLM client implementations (OpenAI, Anthropic)
2. Implement caching for efficiency
3. Add batch processing capabilities
4. Create a web interface
5. Add more sophisticated prompt templates

📝 License

MIT OR Apache-2.0

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Happy Learning! 🦀✨

Updated with comprehensive ThoughtStage theoretical foundation

Last updated: Added comprehensive ThoughtStage theoretical foundation section