A complete log of an AI interaction contains at minimum:

• Timestamp: when the call occurred
• User ID: who asked the question (anonymised or hashed where needed)
• Input: the exact prompt or message sent to the model
• System prompt: the instructions that guide the model's behaviour
• Output: the complete response from the model
• Model and version: which model you used
• Latency: how long the call took
• Tokens used: input and output tokens for cost monitoring
• Error (if applicable): error type and message

Additionally you can log: which tools the model called (with agents), the end-user rating of the answer, and whether the output passed a validation step.

Prompt logs often contain user input that may include personal data. This has implications for GDPR compliance. Consider:

• Anonymisation: replace names and personal data in logs with pseudonyms or hashes
• Retention periods: set a maximum retention period and automatically delete logs after that
• Access control: restrict who can view the logs
• Consent: inform users that interactions are logged where relevant

In regulated sectors such as healthcare or financial services there may be additional requirements for audit logs.

Specific tools have been developed for logging LLM interactions:

• Langfuse: open-source, self-hostable, offers comprehensive trace views and evaluations
• LangSmith: from LangChain, well integrated with that framework
• Helicone: lightweight proxy that intercepts logs without code changes
• Arize: more focused on ML monitoring, also suitable for LLMs

Beyond specialised tools you can also route logs to existing infrastructure: a SQL database for structured logs, a search platform like OpenSearch for full-text search in prompts and responses.

With AI agents that execute multiple steps, you want to log not just the final output but every intermediate step: which tool was called, with which input, and what the output was. This is called tracing.

A good trace shows you how an agent arrived at its answer. That is crucial for debugging: when an agent makes a mistake, you can see exactly at which point it went wrong and why.

Logs are not just for debugging but also for structural improvement:

• Analysis of failed interactions: which questions did the model answer poorly?
• Prompt optimisation: which formulations lead to better output?
• A/B testing: compare two prompt versions based on real usage data
• Cost monitoring: flag prompts that consume unnecessarily many tokens

This is where logging has strategic value. You are not just building a log to fix problems, but to continuously improve your system.

In some contexts audit logs are legally required. An audit log differs from a regular log: it is immutable, complete, and contains enough context to reconstruct after the fact what happened and why.

When you use AI for decisions that affect people — such as a rejection, a recommendation, or a score — you want to (and sometimes must) be able to demonstrate how that decision was reached.

Logging is not an afterthought but a core function of a responsible AI system. At Mach8 we build logging and tracing in from the start of a project, so clients always have insight into what their AI systems are doing and how they are improving.

Curious about how Mach8 makes AI systems manageable and auditable? View our AI agents service or get in touch.