From: aidotengineer
Anthropic is an AI safety and research company focused on building the world’s best and safest large language models (LLMs) [00:01:26]. Founded a few years ago by leading experts in AI, Anthropic has consistently released frontier models while pioneering safety techniques, research, and policy [00:01:34].
Mission and Approach
Anthropic’s daily work involves collaborating with AI leaders who are solving real business problems that seemed impossible just a year prior [00:00:44]. Their mission is to provide actionable insights based on hundreds of customer interactions [00:01:14].
Key Research Directions
Anthropic’s research is distributed across model capabilities, product research, and AI safety, with significant overlap [00:02:25].
Interpretability
A distinguishing research direction for Anthropic is interpretability, which involves reverse engineering models to understand how and why they “think,” and then steering them for specific use cases [00:02:34].
Interpretability research is still in its early stages [00:02:53], but Anthropic approaches it in stages:
- Understanding Grasping AI decision-making [00:03:07].
- Detection Understanding specific behaviors and labeling them [00:03:10].
- Steering Influencing the AI’s input [00:03:15].
- Explainability Unlocking business value from interpretability methods [00:03:22].
In the long term, interpretability is expected to significantly improve AI safety, reliability, and usability [00:03:31]. The interpretability team uses methods to understand feature activations at the model level [00:03:38]. This can lead to a better grasp of the model’s thinking and behavior, or even the discovery of “sleeper agents” for safety reasons [00:03:55].
An example of feature activation is when a model, asked about NBA scores, activates “feature number 304 famous NBA players” when mentioning someone like Steph Curry [00:04:09]. This represents a group of neurons activating in a recognizable pattern across all mentions of famous basketball players [00:04:27].
An example of steering is “Golden Gate Claude,” where activating the “Golden Gate” feature caused Claude to integrate it into unrelated responses, such as suggesting painting a bedroom “red like the Golden Gate Bridge” [00:04:41].
Applying AI and Ensuring Reliability
Anthropic works with customers to embed AI into their products and services [00:08:11], and empowers organizations to use AI in day-to-day work [00:08:14].
They encourage customers to use AI to solve core product problems, moving beyond basic chatbots and summarization to place “bigger bets” [00:05:22]. For instance, in an onboarding and upskilling platform, AI could hyper-personalize course content, adapt dynamically to a user’s pace, or update material based on learning styles [00:06:17].
Anthropic sees AI impacting various industries, including taxes, legal, and project management [00:07:14]. These applications drastically enhance customer experience by making it easier to use and more trustworthy [00:07:22]. For critical workflows like taxes, high-quality output without hallucinations is essential [00:07:38].
Case Study: Intercom’s Finn AI Agent
Anthropic partnered with Intercom, an AI customer service platform with an AI agent called Finn [00:10:58]. They conducted a two-month sprint, optimizing Intercom’s prompts with Claude [00:11:45]. This resulted in Anthropic’s model outperforming Intercom’s previous LLM in benchmarks [00:11:57].
Finn 2, powered by Anthropic, can solve up to 86% of customer support volume, with 51% out of the box [00:12:22]. It also provides a more human element, allowing for tone adjustment, answer length control, and policy awareness, such as refund policies [00:12:35]. This partnership highlights Anthropic’s commitment to creating helpful, reliable, and non-deflecting AI agents [00:12:06].
Best Practices and Common Mistakes in AI Implementation
Anthropic’s applied AI engineering team supports the technical aspects of use cases, helping design architectures, evaluations, and tweak prompts to get the best out of their models [00:09:14]. They also bring customer feedback back to Anthropic to build better products [00:09:23].
Testing and Evaluation
A common mistake is building a robust workflow before designing evaluations [00:13:26]. Evaluations should direct the path to a perfect outcome [00:13:38]. Without proper evaluation, one might just be “trusting the vibes” [00:13:59].
It’s crucial to design representative test cases, including “silly examples” that a user might unexpectedly ask, to ensure the model responds appropriately or reroutes the question [00:15:42]. The model’s behavior in a “latent space” changes with different functions like prompt engineering or prompt caching, and evaluations are the only empirical way to know its performance [00:14:26]. Robust evaluations are considered “intellectual property” that enables outcompeting others [00:15:17].
Identifying Metrics
Organizations often optimize for one or two metrics in the “intelligence-cost-latency triangle” [00:16:16]. This balance should be defined in advance, making a conscious trade-off [00:16:32]. For a customer support use case, low latency (e.g., under 10 seconds) is critical [00:16:40], whereas for a financial research analyst, a longer response time might be acceptable due to the high stakes of the decision [00:16:55]. User experience (UX) considerations, like “thinking boxes” or redirecting users, can also influence latency perception [00:17:21].
Fine-tuning
Fine-tuning is not a “silver bullet” and comes at a cost [00:17:58]. It can limit the model’s reasoning in fields outside the specific fine-tuning domain [00:18:06]. Anthropic encourages trying other approaches first, ensuring clear success criteria, and only resorting to fine-tuning if necessary for a specific intelligence domain [00:18:15].
Other Methods to Explore
Beyond basic prompt engineering, various features and architectures can drastically change the success of a use case [00:19:26]. These include:
- Prompt caching [00:19:40]
- Contextual retrieval [00:19:52]
- Citations [00:20:09]
- Agentic architectures [00:20:11]