Claude Prompt Engineering

Prompts designed for production, validated before they ship

Prompt engineering for a demo is one skill. Prompt engineering for a system that runs thousands of API calls per week, serves 15 different clients with different brand standards, and produces outputs that go directly to customers. that is a different discipline entirely. We write, test, and evaluate production prompts. Our 67-skill agent system runs on prompts that have been iterated against real data and hardened over months of production use. We apply that same rigor to your prompts.

Design Your Prompts

Explore Claude Services

Production-grade prompts in our Skill library

Eval dimensions in our content scoring system

Minimum score threshold before any content ships

100%

Citation rate on our knowledge base prompts

What We Build

Six prompt engineering services for production deployments.

A prompt that works in Anthropic's workbench and a prompt that works reliably in your production system are different things. We build the latter.

System Prompt Architecture

The system prompt is the highest-impact piece of text in your Claude integration. A poorly designed system prompt produces inconsistent behavior that scales badly. We design system prompts with the right structure: persona definition, behavioral constraints, output format specification, example patterns, and edge case handling. Then we test the boundaries: what happens when users try to push Claude outside the intended behavior.

Persona and constraint design
Output format specification
Boundary and adversarial testing
Multi-turn conversation behavior validation

Prompt Caching Strategy

Prompt caching reduces API costs when implemented correctly. The key is identifying which parts of your prompt hierarchy are stable enough to cache and structuring your calls to get the most cache hits. We audit your prompt patterns, identify the stable prefix candidates, add cache_control markers at the right granularity, and measure the before/after token cost impact.

Prompt hierarchy analysis for cache candidates
cache_control marker placement
Cache TTL and invalidation planning
Cost reduction measurement and reporting

Example-Based Prompting

Few-shot examples are one of the highest-impact prompt engineering techniques for output consistency. The right examples. covering the range of inputs your system will encounter, demonstrating the exact output format you want, and showing how to handle edge cases. can double output quality without touching the model. We select and format example sets, evaluate them against your input distribution, and deliver a documented set with coverage rationale.

Example curation for your specific domain
Input distribution coverage analysis
Edge case example design
Example-to-context-window ratio optimization

Structured Output Design

When Claude's output needs to be parsed by downstream systems, the output format is as important as the content. We design JSON schemas, XML structures, and markdown formats that Claude produces reliably. then add validation layers that catch format violations before they reach your application logic. Structured output prompts get tested on 50+ input variants before delivery.

JSON schema and XML format design
Format adherence testing on diverse inputs
Downstream parser validation
Fallback handling for format violations

Eval Framework Development

An eval is a systematic test of a prompt against a defined set of inputs and expected outputs. Without evals, you cannot know whether a prompt change is an improvement or a regression. We build eval frameworks: test case sets, scoring rubrics, automated evaluation scripts, and baseline measurements. Our own content validation system runs 8 eval dimensions on every output. We build the same rigour into your system.

Test case set design (50-200 cases)
Scoring rubric development
Automated evaluation scripting
Baseline and regression tracking

Prompt Performance Analysis

For teams with existing prompts, we run systematic performance analysis: failure mode identification, output variance measurement, latency profiling, and cost-per-quality-unit calculation. The analysis produces a prioritized list of improvements with expected impact. We then implement the highest-value changes and measure the improvement against the baseline.

Failure mode categorization
Output variance and consistency measurement
Cost-per-quality-unit analysis
Prioritized improvement roadmap

How We Engineer Prompts

Four stages from use case to production-validated prompt.

Step 01

Use case analysis and output specification

We start with the output, not the prompt. What does a perfect response look like? What does a failing response look like? How many different input types does the system need to handle? What are the most common failure modes in your current prompts? That analysis drives every design decision that follows.

Step 02

Prompt design and initial testing

We write the prompt, then immediately test its boundaries. Not just the happy path, but the inputs most likely to produce failures: ambiguous inputs, inputs that push toward undesired formats, inputs that test constraint adherence. The first draft is always a hypothesis. The testing tells us whether the hypothesis is correct.

Step 03

Eval framework setup and baseline measurement

We build a test case set covering the input distribution your system will encounter. We define the scoring rubric, set up the evaluation script, and run the baseline measurement. Now we have a number. Every subsequent change gets measured against that baseline. Improvements are real. Regressions are caught before deployment.

Step 04

Iteration against the eval, then production delivery

We iterate on the prompt against the eval framework until we reach the target performance threshold. For caching implementations, we add the cache_control markers and measure the cost impact. The final deliverable is the prompt, the eval suite, the baseline measurements, and documentation on how to run evals when you modify the prompt in the future.

Prompts at Scale

How our 8-dimension eval system validates every output before it ships.

The Challenge

Content delivered to clients must meet two sets of standards simultaneously: it must score above 85 on our 8-dimension anti-AI scoring system (which checks for em-dash usage, AI-predictable vocabulary, sentence rhythm variance, claim specificity, and four other dimensions), and it must meet each client's individual brand voice requirements. Without automated prompt evaluation, these standards are aspirations rather than guarantees.

Our Solution

We built an eval layer on top of every content-generating prompt in our Skill library. Each prompt is designed with explicit brand voice constraints and output format specifications. After Claude generates content, our validate_content.py script runs the 8-dimension scoring check. Outputs that score below 85 feed the specific failure reasons back into the next generation attempt. Prompts that consistently produce below-threshold scores get redesigned at the prompt level, not patched at the output level.

Results Achieved

Eval dimensions measured per output

Automated, before every delivery

Minimum quality threshold

On 100-point scale, hard gate

95%+

First-pass quality gate rate

After prompt redesign cycles

Near zero

Manual QA review rate

For outputs that pass eval gate

Our Eval Methodology

How we measure whether a prompt actually works.

Vibes are not an eval. These are the dimensions we measure across every production prompt we build.

Accuracy

Factual accuracy against ground truth
Format compliance rate across 50+ inputs
Instruction following on specific constraints
Edge case handling quality

Consistency

Output variance across identical inputs
Behavioral stability across turns
Brand voice consistency measurement
Tone adherence scoring

Efficiency

Token usage vs. output quality ratio
Cache hit rate on stable prefixes
Latency benchmarks under load
Context window utilization

Robustness

Behavior under adversarial inputs
Graceful handling of incomplete data
Recovery from upstream failures
Edge case coverage analysis

FAQ

Claude prompt engineering frequently asked questions

Good prompts work on the examples you tested them on. Production prompt engineering is about prompts that work across the full distribution of real inputs your system will encounter, measured against defined quality criteria, tested for robustness, and paired with an eval framework that catches regressions when the prompt changes. The difference shows up at scale, not in demos.

Subjective does not mean unmeasurable. Our 8-dimension content scoring system measures concrete properties: em-dash count, banned vocabulary patterns, sentence length variance, claim-to-evidence ratio, and others. These are proxy metrics for quality, not perfect measures of it. But they are reproducible and automatable. We design eval dimensions that are specific enough to measure automatically and correlated strongly enough with the quality you actually care about.

The right number depends on your task. Simple format tasks often work well with 2-3 examples. Complex tasks with diverse inputs may need 8-12. The more important factor than quantity is coverage: your examples should represent the range of inputs your system will actually receive. One example per input category is more valuable than five examples from the same category.

We run performance analysis on your existing prompts: define the scoring rubric, build the test case set, measure your current prompts against it, identify the failure modes, and rank the improvements by expected impact. Most existing production prompts have 3-5 specific issues that account for the majority of failures. We address those first.

The deliverables are: the final prompt(s), an eval suite with test cases and scoring rubric, a baseline measurement report, documentation on how to run evals when you modify the prompt, and a summary of the design decisions made. You own everything. If you change the prompt in the future, you have the tools to measure whether your change is an improvement.

Ready to Validate Your Prompts?

Let's measure your current performance and improve it.

Prompt audit, eval framework setup, performance measurement, and improvement implementation. You leave with prompts that are measurably better and the tools to keep them that way.

Failure mode analysis on your existing prompts
Eval suite with 50+ test cases included
Before/after performance measurement