COBALTGPT + Cobalt-TunersX – AI-assisted telemetry, diagnostics, and tuning stack for a 2010 Cobalt SS Turbo

Hey everyone,

I’m building an end-to-end telemetry and AI diagnostic stack called TunersX and COBALTGPT. The overarching goal is to transform ChatGPT from a general-purpose assistant into a persistent, domain-specific “Red-Seal mechanic and tuner” for one highly specific vehicle: the 2010 Chevrolet Cobalt SS Turbo.[1]

Since COBALTGPT pushes the boundaries of large language models acting as domain-specific agents, I wanted to share how I’m tackling the architecture. I know many developers here are actively trying to solve these exact agentic workflows, especially regarding context limitations and intention drift.

1. The Domain-Locked System Prompt

Standard models tend to hallucinate or suffer from rapid intention drift when queried on hyper-specific engineering tasks. To counteract this, I utilize an aggressively strict, domain-locked system prompt.[2] The agent is explicitly and repeatedly instructed to only discuss the parameters of the GM Delta platform, the physical limits of the LNF engine, and the exact build sheet of the specific vehicle.[2] By aggressively narrowing the conversational scope, it prevents the AI from offering generic automotive advice and forces it to filter its internal knowledge base strictly for the complex Bosch E69 ECM logic.[2]

2. Persistent Memory-Retention Logic

Automotive builds and complex diagnostics often span months or years, which fundamentally breaks the traditional ephemeral chat session.[1] To solve this, I built a dedicated memory layer designed to persistently retain critical information across infinite sessions.[1] This layer actively stores and updates a live, full build sheet tracking every aftermarket part, the vehicle’s complete maintenance history, and baseline tuning assumptions (like current fuel octane, power goals, and target boost pressure).[2] Utilizing memory-based correction logic ensures the AI constantly understands the vehicle’s current state without requiring the user to restate the context every time they log in.[1]

3. Context-Window Management

Feeding raw automotive network data into an LLM will immediately obliterate a context window. To manage this, the hardware/software layer (Cobalt-TunersX) acts as a highly structured filter. It captures raw hexadecimal CAN bus data, decodes it using DBC files, and packages it into structured “trace bundles”.[3] Instead of dumping raw data streams into the prompt, the AI only ingests these refined trace bundles containing specific decoded events (like torque management interventions, engine load, or knock retard).[2, 3] This keeps the context window lean and reserves token usage strictly for the AI’s diagnostic reasoning and root-cause analysis.[3]

This project has proven to me that AI-assisted mechanical and tuning systems are inevitable, but we still desperately need better long-context control, persistent multi-file memory, and dedicated user-defined toolchains to make these architectures scale gracefully.[1]

I’d love to connect with anyone else working on indefinite looping or long-term memory for domain-specific agents. How are you handling token compression when your agent’s historical state gets too large?