$ cat services.json

Prompt Optimization

Improve existing prompts for better quality and lower costs.

Deliverables:

Prompt audit and analysis
Quality improvement
Token reduction
Consistency improvements
A/B testing framework

Structured Output Design

Design prompts that produce reliable, parseable outputs.

Deliverables:

JSON/XML output schemas
Validation strategies
Error handling
Pydantic integration
Instructor patterns

Prompt System Architecture

Design prompt systems for complex applications.

Deliverables:

Prompt templates
Chain-of-thought patterns
Multi-step reasoning
Context management
Prompt versioning

$ man prompt-engineering

Prompt Engineering Patterns

Chain of Thought (CoT)

Break complex problems into steps
Improve reasoning accuracy
Show work for debugging

Few-Shot Learning

Provide examples in prompt
Guide output format
Improve consistency

Structured Output

Define JSON/XML schemas
Use Pydantic validation
Ensure parseable results

System Prompts

Set behavior and constraints
Define persona and tone
Establish guardrails

Cost Optimization Techniques

I’ve helped reduce LLM costs by 40-50% through:

Token reduction: Concise prompts without losing context
Model routing: Use cheaper models for simple tasks
Caching: Store results for common queries
Batching: Combine similar requests
Output limits: Request only needed length

$ cat README.md

Prompt Engineering Patterns

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from pydantic import BaseModel
from openai import OpenAI

# Structured Output with Validation
class DocumentAnalysis(BaseModel):
    summary: str
    key_entities: list[str]
    sentiment: str
    confidence: float
    citations: list[dict]

ANALYSIS_PROMPT = """
Analyze the following document and extract structured information.

<document>
{document_text}
</document>

Think through this step by step:
1. First, identify the main topic and purpose
2. Extract key entities (people, organizations, dates)
3. Determine overall sentiment
4. Note any citations or references

Respond with valid JSON matching this schema:
{schema}

Be precise and include confidence scores.
"""

def analyze_document(text: str) -> DocumentAnalysis:
    response = client.chat.completions.create(
        model="gpt-4-turbo",
        messages=[{
            "role": "user",
            "content": ANALYSIS_PROMPT.format(
                document_text=text,
                schema=DocumentAnalysis.model_json_schema()
            )
        }],
        response_format={"type": "json_object"}
    )
    return DocumentAnalysis.model_validate_json(response.choices[0].message.content)

Prompt Optimization Checklist

Technique	Impact	When to Use
Be specific	Quality ↑	Always
Show examples	Consistency ↑	Complex formats
Chain of thought	Accuracy ↑	Reasoning tasks
Output schema	Reliability ↑	Data extraction
Temperature tuning	Control ↑	Balance creativity/consistency
Token reduction	Cost ↓	High-volume applications

Experience:

Case Studies: LLM Email Assistant | Multi-LLM Orchestration | Enterprise RAG System

Related Technologies: OpenAI, Anthropic Claude, LangChain, RAG Systems

$ ls -la projects/

Legal Document Analysis

@ Anaqua

Challenge:

Extract structured data from patent documents with high accuracy.

Solution:

Chain-of-thought prompts with structured JSON output, validation, and confidence scoring.

Result:

95%+ extraction accuracy, suitable for production legal work.

Email Generation

@ Flowrite

Challenge:

Generate professional emails matching user's writing style.

Solution:

Few-shot prompts with style examples, tone control, and length optimization.

Result:

High user satisfaction, 10x growth to 100K users.

Code Analysis Agent

@ Sparrow Intelligence

Challenge:

Analyze codebases and answer developer questions accurately.

Solution:

Multi-step reasoning prompts with code context, structured analysis output.

Result:

Accurate, helpful responses for complex code questions.

✍️ Prompt Engineering

$ cat services.json

Prompt Optimization

Structured Output Design

Prompt System Architecture

$ man prompt-engineering

Prompt Engineering Patterns

Cost Optimization Techniques

$ cat README.md

Prompt Engineering Patterns

Prompt Optimization Checklist

$ ls -la projects/

Legal Document Analysis

Email Generation

Code Analysis Agent

$ diff me competitors/

Optimize Your Prompts