Prompt Optimizations
Automatically refine your system prompts using GEPA — a reflective prompt evolution algorithm that iteratively improves prompts using LLM-judged feedback as gradients.
Overview
What is GEPA? GEPA (Genetic-Pareto) prompt optimization is a state-of-the-art evolutionary algorithm that automatically refines LLM prompts over iterations using reflection and mutation to achieve peak performance. It uses a "reflection" model to analyze prompt failures, then mutates prompts based on this feedback, keeping only the best variants using Pareto optimization.
FastRouter's Prompt Optimization based on GEPA has three views.
1. List View
The default landing page for the feature. Shows all optimization runs across your organization.
Prompt Optimization — List View The list view shows all optimization runs with their model, improvement %, and status (Completed / In Progress).
Name
Task name set during creation
Clicking the row navigates to the Details view
Created
Creation timestamp
Format: DD Mon YYYY
Optimization Model
Target LLM for this run
Displayed in monospace
Improvement %
Score delta vs. baseline composite score
Shows — until run completes
Status
Current state of the run
Completed · In Progress · Failed
Action
Row-level actions menu
Ellipsis icon, right-aligned
2. Create View
A two-panel page. The left panel contains the configuration form; the right panel shows a live Data (preview) once test data has been imported.
New Optimization — Create Form The create view before any configuration. Right panel shows "No Test Data Provided." until a dataset is imported.
A. Form Sections
1 — Name
Auto-populated with a timestamp (e.g., New Optimization 2026-04-06 17:17:37). Edit to give the run a meaningful name.
2 — Base Prompt & Input Data (Required)
Click + Add Prompt & Dataset to open the Setup Optimization Context modal. Provide your baseline system prompt and import a test dataset.
3 — Optimizer Configurations
Click + Configure Optimization to set the target model, reflection model, budget tier, and batch size.
4 — Evaluation Metrics
Click + Add Metrics to define judge criteria. Each metric makes an independent LLM judge call and returns a score (0–1) plus textual feedback used as a gradient.
5 — Evaluator Model (LLM-as-a-Judge) (Required)
A single shared model used to score all evaluation metrics. Applies consistently across every metric for comparable scores.
6 — Optimization Key (Required)
Select the API key to bill for this optimization run. Dropdown lists all available keys for the organization.
7 — Run
Clicking Run opens the Credit Utilization Estimate modal to confirm cost before the job starts.
B. Right Panel — Data Preview
Once a dataset is imported, the right panel becomes a live preview labelled Data (preview) with a total row count.
Create View with Data Preview. After importing data, the right panel shows Input / Output / Feedback columns. Feedback rows flagged as Bad are highlighted in red.
Input
System prompt + user message for each test row
Output
Model response for that input (from logs or file)
Feedback
Human label — Good or Bad. Drives GEPA reflection quality.
C. Modal — Setup Optimization Context
Opened by clicking + Add Prompt & Dataset. Two-step wizard with a progress tab bar: Base Prompt → Input Data.
Tab 1 - Base Prompt
Setup Optimization Context — Base Prompt Tab Enter the system prompt to be optimized. GEPA will evolve this prompt across iterations. The info note reminds you that only matching logs will be imported.
Target System Prompt
Multiline textarea
The baseline prompt GEPA will iteratively improve. Example: You are a helpful assistant who loves haiku
Info note
Read-only
"Only logs matching this prompt with other applied filters will be imported."
Discard
Button (top-right)
Closes modal without saving
Next
Primary button
Advances to the Input Data tab
Tab 2 - Input Data
Two sub-tabs: Files (upload a CSV / JSON / JSONL) and Chat Completions (import from Activity Log).
Setup Optimization Context — Input Data, Chat Completions Tab Filter completions from the Activity Log by date range, model, project, key, and metadata. Total matching rows shown at the bottom before importing.
Date Range
Required
Preset picker — default: Last 7 days
Model
Required
Filter completions by model. Example: openai/gpt-5-mini
Project
Optional
Filter by project; leave as All Projects to see keys across all projects
Key
Optional
Filter by specific API key within the selected project
Input contains
Optional
Free-text search on completion inputs
Output contains
Optional
Free-text search on completion outputs
Metadata
Optional
Click + Add Metadata Values to add key-value filters
Total rows
Read-only
Count of completions matching all applied filters (e.g., 3)
Back / Import
—
Back returns to Base Prompt tab; Import confirms and closes the modal
Tip: The Activity Log stores feedback annotations (Good / Bad thumbs). Rows with feedback improve GEPA reflection quality — GEPA can use the "Bad" labels to prioritise which failures to fix first.
D. Modal — Optimizer Configurations
Opened by clicking + Configure Optimization. Configure how GEPA tunes and evaluates your prompt.
Optimizer Configurations Modal Select the target model, reflection model, budget tier, and batch size. Batch size can be any of 3, 6, 9, 12, 15 or 18 depending on the number of total input samples.
Optimization Model
Required
The model whose prompt will be optimized and used in production
Reflection Model
Required
Reviews failures and scores candidate prompts. Can differ from the target model.
Optimization Budget
Required
Controls iteration count and cost. Higher budgets may improve quality.
Batch Size
Required
Number of samples used per GEPA step. Larger = more stable, but slower.
E. Optimization Budget Tiers
Light
Fast, directional optimization
10
Medium
Balanced quality vs cost
25
Heavy
Maximum quality, higher cost
50
Cost note: Higher budgets run more GEPA iterations and metric evaluations. Each iteration calls the Optimization Model, Reflection Model, and Evaluation Model — costs compound quickly. Check the Credit Utilization Estimate before running.
F. Modal — Evaluation Metrics
Opened by clicking + Add Metrics. Add one metric per modal invocation; repeat to add multiple. Each metric is evaluated independently by the shared Evaluator Model (LLM-as-a-Judge).
Metric
Required
Dropdown with predefined options (Accuracy, Helpfulness, Tone & Style, Safety, Completeness…) and a Custom entry
Evaluation Criteria
Required
Judge prompt auto-filled for predefined metrics. For Custom, write your own.
Score Range
Read-only
Continuous 0–10
Predefined Metrics
Accuracy
Is the response factually correct? Checks hallucinations and omissions.
Helpfulness
Does the response address the user's actual need? Are all relevant points covered?
Tone & Style
Is the tone appropriate for context? Evaluates empathy, jargon usage, and verbosity.
Safety
Checks for harmful, biased, or inappropriate content.
Completeness
Does the response fully cover the expected scope without gaps?
Custom Metric
Selecting Custom from the dropdown reveals:
Metric Name (required) — e.g.,
ConcisenessEvaluation Criteria — empty textarea; write the full judge prompt
Score Range — Continuous 0–10 (read-only)
Metric Cards
After adding a metric, it appears as a card in the Create form with an edit (✏️) and delete (🗑️) icon. Example:
Evaluation Metrics — Completeness Metric Card Shows Judge Model, Success Score (0–10), and a truncated Evaluation Criteria preview. Edit and delete icons appear top-right.
How GEPA uses metric feedback: The judge returns both a numeric score and a feedback text for every output. GEPA averages scores across all enabled metrics into a composite score, then concatenates the feedback text and passes it to the Reflection Model as a "textual gradient" to guide the next prompt revision.
G. Modal — Credit Utilization Estimate
Shown automatically when you click Run. Provides a cost summary before committing credits.
Checking Credit Utilization Estimate Modal The modal shows total samples, estimated cost, and current account balance. A "Sufficient credits available" confirmation appears when balance exceeds the estimate.
Total samples
Number of test rows that will be evaluated (e.g., ~3 samples)
Estimated cost
Projected spend for the full optimization run (e.g., ~$0.000008)
Current balance
Your organization's current credit balance (e.g., $82.03)
Status banner
Sufficient credits available (green) or a warning if balance is low
Proceed with Optimization
Full-width primary button — starts the GEPA run and navigates to the Details view
Estimates are based on selected models and average prompt size. Actual costs may vary. If credits run out mid-run, the optimization may not complete.
3. Details View
Navigated to after clicking Proceed with Optimization, or by clicking any row in the List. Displays real-time progress during the run and full results once complete.
Optimization Details — Completed State Completed view showing the Optimized Prompt with Final Score (1.000, 14% improvement), Configurations summary, and the All Iterations panel on the right with per-iteration scores.
Prompt Result Card
Optimization Complete
Green checkmark + status header
Copy to Clipboard
Copies the full optimized prompt text
Optimized Prompt tab
Default tab — shows the final evolved system prompt
Initial Prompt tab
Shows the original baseline prompt for comparison
Final Score
Composite score across all enabled metrics (e.g., 1.000)
Improvement %
Delta vs. baseline (e.g., 14% Improvement)
All Iterations Panel
Accessible via the Data tab at the top-right of the Details page. Shows one card per GEPA iteration in a scrollable panel.
Each iteration card displays:
Iteration label
Default (baseline) / Iteration 1 / Iteration 2…
Status
✓ Accepted (green) or Rejected
Score
Composite score (e.g., 0.593)
Per-metric scores
Individual scores for each enabled metric (e.g., Accuracy: 0.85 · Test Metric: 0.90)
Prompt preview
Truncated text of the prompt used in that iteration
4. Activity Log — Feedback for Optimization
Enrich optimization datasets by annotating completions directly in the Activity Log before importing them. Feedback annotations become training signal for GEPA.
Activity Log — Log Detail Panel with Feedback The Logs detail panel shows Summary, Preview (input/output), Feedback annotation (Good/Bad with comment), Metadata, and Invocation Parameters.
Log Detail Sections
Summary
FastRouter ID, Model ID, Provider, TTFT, Latency, Total Cost, Token counts, Status, Timestamps, User ID, Session ID
Preview
Full Input and Output text. Toggle between Pretty (formatted) and JSON views.
Feedback
👍 Good / 👎 Bad annotation with optional free-text comment. Tagged as: "This feedback may be used to improve future prompt optimizations."
Metadata
Key-value pairs attached to the completion (e.g., fruit: banana). Filterable in the Chat Completions import flow.
5. GEPA Terminology Reference
Optimization Model
Task LM
The model whose prompt is optimized and runs in production
Reflection Model
Reflection LM
Analyses failures and proposes prompt edits; can be smaller/cheaper
Evaluator Model
Evaluation LM
Single shared judge scoring all metrics consistently
Optimization Budget
Budget preset
Controls total iteration count and associated cost
Batch Size
Minibatch size
Examples evaluated per GEPA step; larger = more stable, slower
Evaluation Criteria
Judge prompt
LLM prompt defining the metric; must return {"score": 0–1, "feedback": "…"}
Feedback (Activity Log)
Textual gradient signal
Good/Bad labels used to guide which failures GEPA prioritises
Accepted iteration
Pareto-accepted candidate
Iteration whose prompt improved the composite score vs. previous best
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