Shreya Rajpal

Co-founder & CEO, Guardrails AI · 1 appearance on the record.

computed by AI from the episodes · how this works → · full disclaimer →

founderexecutiveengineer@ShreyaR ↗LinkedIn ↗shreya-rajpal.com ↗

Shreya Rajpal is the co-founder and CEO of Guardrails AI and creator of Snowglobe, platforms focused on mitigating AI hallucinations and testing agents. She previously built machine learning models and infrastructure at Drive.ai, Apple's Special Projects Group, and Predibase.

7statements → 1claims → 0claims resolved → 3.86/5average certainty → 2.71/5average debate potential → 4.3/5argument clarity · the sources →

1 not checkable as stated how the 1 claim stands · each chip opens the sources

1 assertion · 1 opinion · 5 insights · every statement was checked. The predictions and assertion are the 1 claim: statements the public record can support or contradict. 0 are resolved, and 1 names no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.

The record, in short

What the tape says about how Shreya argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.

Argument clarity: do they answer the question? how? →

4.3 / 5 directness 4.6 · coherence 4.6 · precision 4.1 · compression 3.5

answered every one of 8 assessed questions directly

This is a score against a rubric. It is not a rank. Every host question → answer exchange is scored with names hidden on directness, coherence, precision and compression, 1–5 each, on meaning alone: disfluencies are ignored, and only raw unedited episodes count. This is the score that measures thought. Every scored exchange, scores shown → · The rubric and its checks →

How they sound: speaking style how? →

235 words/min while actually speaking · 54.4 um and uh per 1k words

Measured by listening to the audio itself: 8,112 words across 1 episode of raw-level tape, transcribed verbatim with every um and uh kept, each one attributed only where the alignment onto our timed stream is unambiguous. These are measurements of speaking style. We do not rank them: across this corpus, fluency and argument quality are nearly uncorrelated (ρ≈0.2), and smooth talking does not signal clear thinking. How it's measured →

What they said7 Episodes1

Everything Shreya Rajpal said on the MAD Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

Insight

Production LLM deployment challenges mirror autonomous vehicle development

“The trajectory of issues and concerns that people are running into are similar to, you know, like the path is similar to what it was in self-driving, which is, you know, How do I get like this, you know, runtime safety? How do I get like runtime constraints, e…”

Shreya Rajpal Sep 27, 2023 ▶ 4:56 Guardrails AI: The Playbook for Safer, Hallucination-Free LLMs — Shreya Rajpal Explains

Insight

Existing model risk frameworks fail for third-party AI models

“Existing, for example, like model risk management frameworks don't really apply when you haven't built the model yourself. You know, you didn't like curate the data that the model was trained on. And so you can't make any claims to that.”

Shreya Rajpal Sep 27, 2023 ▶ 7:10 Guardrails AI: The Playbook for Safer, Hallucination-Free LLMs — Shreya Rajpal Explains

Insight

Fine-tuning cannot eliminate LLM hallucinations

“At the end of the day, these models are like next token predictors, which is, you know, like they kind of look at like what they've predicted until now, and then, you know, figure out like what the next token they're on is. And from that, like, even with fine-…”

Shreya Rajpal Sep 27, 2023 ▶ 17:46 Guardrails AI: The Playbook for Safer, Hallucination-Free LLMs — Shreya Rajpal Explains

Insight

GenAI solves ML's first mile, but traditional tools handle the last

“LLMs and Generative AI really helps solve, like the first mile problem in ML, right? But the last mile problem, which is, like, how do you take this generic generalizable technology and make it work, like, specifically for your use cases and for your actual ap…”

Shreya Rajpal Sep 27, 2023 ▶ 34:43 Guardrails AI: The Playbook for Safer, Hallucination-Free LLMs — Shreya Rajpal Explains

Assertion Not checkable as stated

Fine-tuned Llama 2 achieves performance comparable to GPT-3.5 and GPT-4

“Straight out of the bat, if you just use Lama tool directly, I don't think you could get like comparable performance, you know, with GPD, 3.5 or four today. But like with fine tuning, if you make that investment in curating your data set in running that fine t…”

Shreya Rajpal Sep 27, 2023 ▶ 22:48 Guardrails AI: The Playbook for Safer, Hallucination-Free LLMs — Shreya Rajpal Explains

Opinion

RAG is the definitive way to build generative AI today

“Rag is the way to build you know, these models today”

Shreya Rajpal Sep 27, 2023 ▶ 46:33 Guardrails AI: The Playbook for Safer, Hallucination-Free LLMs — Shreya Rajpal Explains

Insight

Open source turns abstract AI safety into an actionable engineering problem

“What the open source really ends up doing as you know, a framework is taking down this very abstract problem of what it means to do safe AI development, right? Like it's a very abstract problem. It's almost an academic problem to some degree, and it takes that…”

Shreya Rajpal Sep 27, 2023 ▶ 41:47 Guardrails AI: The Playbook for Safer, Hallucination-Free LLMs — Shreya Rajpal Explains

Appearances (1)

Episode	Date	Speaking time
Guardrails AI: The Playbook for Safer, Hallucination-Free LLMs — Shreya Rajpal Explains	Sep 27, 2023	39m

Shreya Rajpal

The record, in short

Argument clarity: do they answer the question? how? →

How they sound: speaking style how? →

Appearances (1)

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