In this episode, we talk about practical guardrails for LLMs with data scientist Nicholas Brathwaite. We focus on how to stop PII leaks, retrieve data, and evaluate safety with real limits. We weigh managed solutions like AWS Bedrock against open-source approaches and discuss when to skip LLMs altogether.

• Why guardrails matter for PII, secrets, and access control
• Where to place controls across prompt, training, and output
• Prompt injection, jailbreaks, and adversarial handling
• RAG design with vector DB separation and permissions
• Evaluation methods, risk scoring, and cost trade-offs
• AWS Bedrock guardrails vs open-source customization
• Domain-adapted safety models and policy matching
• When deterministic systems beat LLM complexity

This episode is part of our "AI in Practice” series, where we invite guests to talk about the reality of their work in AI. From hands-on development to scientific research, be sure to check out other episodes under this heading in our listings.

Related research:

• Building trustworthy AI: Guardrail technologies and strategies (N. Brathwaite)
• Nic's GitHub

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We’re excited to have Adi Ganesan, a PhD researcher at Stony Brook University, the University of Pennsylvania, and Vanderbilt, on the show. We’ll talk about how large language models LLMs) are being tested and used in psychology, citing examples from mental health research. Fun fact: Adi was Sid's research partner during his Ph.D. program.

Discussion highlights

• Language models struggle with certain aspects of therapy including being over-eager to solve problems rather than building understanding
• Current models are poor at detecting psychomotor symptoms from text alone but are oversensitive to suicidality markers
• Cognitive reframing assistance represents a promising application where LLMs can help identify thought traps
• Proper evaluation frameworks must include privacy, security, effectiveness, and appropriate engagement levels
• Theory of mind remains a significant challenge for LLMs in therapeutic contexts; example: The Sally-Anne Test.
• Responsible implementation requires staged evaluation before patient-facing deployment

Resources

To learn more about Adi's research and topics discussed in this episode, check out the following resources:

• Large language models could change the future of behavioral healthcare: a proposal for responsible development and evaluation
• Therapist Behaviors paper: [2401.00820] A Computational Framework for Behavioral Assessment of LLM Therapists
• Cognitive reframing paper: Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction - ACL Anthology
• Faux Pas paper: Testing theory of mind in large language models and humans | Nature Human Behaviour
• READI: Readiness Evaluation for Artificial Intelligence-Mental Health Deployment and Implementation (READI): A Review and Proposed Framework
• Large language models could change the future of behavioral healthcare: A proposal for responsible development and evaluation | npj Mental Health Research
• GPT-4’s Schema of Depression: Explaining GPT-4’s Schema of Depression Using Machine Behavior Analysis
• Adi’s Profile: Adithya V Ganesan - Google Scholar

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The release of OpenAI GPT-5 marks a significant turning point in AI development, but maybe not the one most enthusiasts had envisioned. The latest version seems to reveal the natural ceiling of current language model capabilities with incremental rather than revolutionary improvements over GPT-4.

Sid and Andrew call back to some of the model-building basics that have led to this point to give their assessment of the early days of the GPT-5 release.

• AI's version of Moore's Law is slowing down dramatically with GPT-5
• OpenAI appears to be experiencing an identity crisis, uncertain whether to target consumers or enterprises
• Running out of human-written data is a fundamental barrier to continued exponential improvement
• Synthetic data cannot provide the same quality as original human content
• Health-related usage of LLMs presents particularly dangerous applications
• Users developing dependencies on specific model behaviors face disruption when models change
• Model outputs are now being verified rather than just inputs, representing a small improvement in safety
• The next phase of AI development may involve revisiting reinforcement learning and expert systems
* Review the GPT-5 system card for further information

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This summary was AI-generated from the original transcript of the podcast that is linked to this episode.

What did you think? Let us know.

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