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Testing the AI Behind the Curtain An Inside Look at Automating Safeguards for Language Models

Testing the AI Behind the Curtain

Amid rapidly expanding enterprise adoption of large language models (LLMs), establishing guardrails to validate accuracy and control risks has become an urgent priority. However, comprehensively monitoring unpredictable large language models is enormously complex, requiring specialized expertise and effort.

UC Berkeley and LangChain researchers have recently open-sourced an intriguing solution called “spade” which auto-generates safety tests tailored to LLM-based data pipelines. By automatically tracking prompt engineering revisions, spade can catch emerging failure modes and generate corresponding Python assertions without intensive manual oversight.

The Promise and Peril of Enterprise Language Models

LLMs like GPT-4, Claude, and even private LLMs like Palantir’s, offer tremendous simplicity for building automated natural language generation workflows. However, their opacity and eccentricities pose formidable hurdles to deployment at scale. Using LLMs at scale surfaces dependences between phrasing, order of instructions, and output integrity. Dealing with these inconsistencies means reliability disasters lurk across prompts and versions.

Small changes in prompts can significantly affect prompt outputs as we see in this example.

Prompt v1: "Summarize this article about global warming solutions."
Output: Broad commentary about climate change debates
Prompt v2: "Summarize solutions in this article about global warming."
Output: Focused technical analysis of emissions reduction policies

All this adds up to trouble for AI engineering development. Comprehensive monitoring and validating designed to prevent downstream failures require tremendous expertise and effort. Manual testing approaches cannot handle the vast array of unpredictable errors and edge cases.

An Inside Look at spade in Action

To see how the new LangChain spade system might work, let’s imagine ourselves as AI Engineers. This new type of enterprise architect and system integration leader works to orchestrate the backend processes involved in delivering AI apps. Now, let’s examine a fictional AI Engineering scenario to better illustrate spade’s integration and impact within the software development lifecycle (SDLC) of an enterprise LLM pipeline.

A quantitative Private Equity firm is building an app asking GPT-4 to analyze tickers and holdings in client investment portfolios. For each portfolio composition, GPT-4 generates a risk assessment report drawing from associated 10-K filings.

After initial prototyping and internal dogfooding, the development team releases a beta version of the 10-K analysis pipeline to their analyst team. However, as real-world usage volumes grow, the analysts soon begin noticing concerning inaccuracies in GPT-4’s programmatic assessments.

Sometimes company names are misspelled or omitted altogether. Numbers in the quantitative financial summaries often fail to precisely reconcile across sections. There are even sporadic instances of insensitive or biased language unacceptable for client reports.

Overall, the analysts must manually review all LLM-generated outputs before sending them to portfolio managers, severely denting workflow efficiency and user experience.

Auto-Assertions to the Rescue

Our fictional development team realizes they need to rigorously safeguard model reliability and output integrity before full production rollout. But manually formulating comprehensive Python assertions to catch all potential vagaries in GPT-4 ‘s assessments – missing or misrepresented companies, numerical discrepancies, inappropriate terminology – will be extremely intricate and time-intensive. Constantly editing scores of intricate regex checks will dramatically slow overall iteration velocity.

Instead, they decide to streamline the process by onboarding spade’s auto-generated LLM assertions.

In the initial GPT-4 query template, placeholders are provisioned for portfolio tickers symbols and composition ratios. Spade first constructs a diff between this baseline prompt and any subsequent updated versions – for example, adding explicit instructions for GPT-4 to avoid racial stereotypes or accurately represent financial dilution effects from options vesting.

Delta analysis automatically generates Python functions to validate compliance with each newly introduced prompt criteria using libraries like NumPy and subqueries to external APIs.

These auto-generated assertions then seamlessly integrate with the call workflow – appending to every GPT-4 invocation as parameters. Over a few weeks, spade also progressively learns interactively – continually refining checks based on any newly analyst-flagged errors or concerns logged by the app.

Assuring Continuity in Enterprise AI Workflows

Armed with spade’s auto-generated guardrails codifying allowlists, blocklists, and credibility checks, the VC portfolio management team can confidently scale GPT-4 augmentation across thousands more market analyses. Analysts can focus uniquely on highest-value human oversight roles. Any unfavorable drifts detected in input-output integrity will automatically trigger failsafe actions, preventing unsafe propagation.

As backend models and pipelines inevitably evolve, front-facing query templates can remain highly resilient via auto-generated safety nets dynamically tracking tipping points. Spade converted a previously perilous scaled rollout into assured continuity, cementing trust in AI-augmentation. Enterprise leaders described this transformation as pivotal in their team’s productization journey with generative language models.

Contact the spade Team at LangChain

This research on auto-generating evaluations for LLM pipelines is spearheaded by researchers from UC Berkeley, Columbia University, and San Francisco-based LangChain. The team has released an alpha prototype of the spade system that developers can access to try out suggested assertions tailored to their own prompts.

I encourage readers to visit the LangChain blog post covering this innovation to learn more and provide feedback. The post contains links to test drive spade’s capabilities on custom chains and connect with the researchers if you would like to get involved.

As LLMs continue advancing rapidly, auto-generated safeguards like spade can meaningfully enhance responsible adoption. The system has shown promising capability to catch errors, reduce false positives, and boost productivity. However, improving real-world impact necessitates community participation.

Please consider playing with the prototype and sharing your experience. You can also join the open-source project if interested to help drive spade’s progress. Let us collectively uphold ethical AI values and enable more users to harness LLMs safely. I hope you will visit the LangChain blog and potentially get engaged with this high-potential project.