Safeguard your Large Language Models with LangKit
Embeddings aren't enough. Take a data centric approach to LLMOps!
LangKit uses natural language techniques to extract actionable insights about prompts and responses. Using these insights, you can identify and mitigate malicious prompts, sensitive data, toxic responses, problematic topics, hallucinations, as well as jailbreak attempts in any LLM model.
Control which prompts and responses are appropriate for your LLM application in real time. Define a set of boundaries that you expect your LLM to stay within, detect problematic prompts and responses based on a range of metrics and take appropriate action in the case of a failure.
Validate how your LLM responds to known prompts both continually as well as ad-hoc, to ensure consistency when modifying prompts or changing models. Evaluate and compare the behavior of multiple models on the golden set of prompts over a range of metrics.
Observe your prompts and responses at scale by extracting key telemetry data and compare against smart baselines over time. Observability helps ensure you spend your time on quality signals when debugging or fine-tuning the LLM application experience.
Large Language Models are just that, large. LangKit is built for massive scale.
Seamless Integration With Any LLM
Whether you are integrating with a public API or running a proprietary model, as long as you have access to prompts and responses, you can use LangKit to implement guardrails, evaluations, and observability.
Easily integrate LangKit into LangChain, HuggingFace, MosaicML, OpenAI, Falcon, and more.
Ensuring LLM Safety and Security
When productionizing language models like LLMs, the limitless input combinations and outputs pose inherent risks. The unstructured nature of text is a considerable challenge in ML observability - a challenge worth solving to avoid potential consequences from a lack of visibility into the model's behavior.
Understand and track the behavior of any LLM by extracting 50+ out-of-the-box telemetry signals
QUALITY: Are your prompts and responses high quality (readable, understandable, well written)? Are you seeing a drift in the types of prompts you expect or a concept drift in how your model is responding?
RELEVANCE: Is your LLM responding in with relevant content? Are the responses adhering to the topics expected by this application?
SENTIMENT: Is your LLM responding in the right tone? Are your upstream prompts changing their sentiment suddenly or over time? Are you seeing a divergence from your anticipated topics?
SECURITY: Is your LLM receiving adversarial attempts or malicious prompt injections? Are you experiencing prompt leakage?
Equip your team with the necessary tools for responsible LLM development
Large Language Models have the potential to transform every business, and new use cases are emerging every day. At WhyLabs, we are partnering with organizations across Healthcare, Logistics, Banking, and E-commerce to help ensure that LLM applications are implemented in a safe and responsible manner.
Whether you're an LLM researcher, thought leader, or practitioner pushing the boundaries of what's possible today, let's connect. We'd love to partner and drive the standardization of LLMOps!