Synthetic Instruction Data Generation with Automated Sandbox Validation
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About the Project
This system was engineered for an enterprise telecommunications and networking hardware vendor looking to fine-tune an internal language model for autonomous network diagnostics and CLI troubleshooting.
Historical human customer support tickets were scarce, unstandardized, and filled with incomplete diagnostic logs. The goal was to synthesize a mathematically diverse, technically accurate dataset of 120,000 instruction-response pairs covering complex routing protocols and switch configurations.
Challenges We Faced
1. Syntactic Homogeneity & Template Memorization
Naive prompting loops asking foundation models to generate technical support dialogues repeatedly produced the same 10 phrasing patterns. Models fine-tuned on homogeneous data memorized stylistic templates rather than learning causal diagnostic reasoning.
2. Hallucinated CLI Commands and Fictional RFC Standards
Generative models frequently invented non-existent flags for router operating systems (Cisco IOS, Junos OS) or cited fictional networking RFC standards, which would cause catastrophic network outages if applied to customer hardware.
3. Prohibitive Cost of Manual Expert Auditing
Having senior CCIE network engineers manually review 120,000 synthesized troubleshooting dialogues was financially unviable, requiring a fully automated, deterministic validation pipeline.
Solution Architecture
Acadify architected an automated synthesis and deterministic verification pipeline, delivered through our AI testing and evaluation practice.
We adapted the Evol-Instruct methodology to systematically increase problem difficulty, combined with an automated Docker sandbox that executed every synthesized terminal command against real virtual networking operating systems.
- Clustered Evol-Instruct Engine: Generated instructions across 24 networking domains. Generated prompts were embedded and clustered using k-means in Qdrant; over-represented clusters were dynamically penalized to force the generator into edge-case diagnostic scenarios.
- Deterministic Sandbox CLI Verification: Every generated command was dispatched to an air-gapped Docker container running real network OS emulators. Any dialogue that generated a syntax error, illegal flag, or invalid parameter was immediately discarded.
- Automated Multi-Criteria Scoring: Implemented an LLM-as-a-judge scoring rubric evaluating logical reasoning, diagnostic completeness, and safety constraints. Ambiguous edge-case generations were automatically routed to a human engineering review queue.
Why Acadify's Engineering Approach Fit the Project
Generating high-quality synthetic data is not a prompting exercise; it is a compiler and verification challenge. Acadify pairs generative language models with deterministic sandbox environments, ensuring every synthesized training pair is technically executable and verified against real system runtimes.
System Architecture & Tech Stack
Related Acadify Solution Services
This case study demonstrates capabilities from Acadify Solution's Data Annotation Services and AI Testing & Evaluation practices.