Journey Through Innovation: The LLM Adventure

Manouk

Apr 8, 2024

Last week we spoke with one of our launching customers about how they experience LangWatch at this moment. We came to a great storyline which I’d love to share with you here, so let me walk you through this.

LangWatch goal is to provide a repeatable process that acts as a guiding light for those embarking on the journey of AI integration. This article aims to explore the process, offering insights, strategies, and practical steps that empower decision-makers to leverage the full potential of AI within their organizations.

πŸš€ Phase 1: First Touch πŸš€

It all starts with curiosity. Companies and teams get their hands on LLMs (Large Language Models), playing around, and experimenting. With certain tools and various LLM's out there, they quickly move to create a first proof of concept. Whether this is for internal usage (mostly first) or external use cases - it all start with a first proof of concept.

Start small: This approach is designed to propel organizations forward, enabling them to achieve quick wins through the deployment of modest AI initiatives, including applications like internal Q&A, chatbot technologies, and content generation tools.

Especially beneficial for entities either at the early stages of AI adoption or those operating with limited resources, this strategy offers an good opportunity for experimentation and insight. It acts as a preliminary step, allowing businesses to dip their toes into the vast ocean of AI integration, thereby gaining practical experience and understanding of how AI can be woven into their operational fabric.

πŸ” Phase 2: Testing the Waters πŸ”

Put this proof of concept β€œsomewhere” - at least for internal testing with a first batch of users (employees). See how it behaves, how it works - manually looking at the first couple of messages. Engaging end-users in the development process is crucial to instilling trust in the output generated by the GenAI models. Engaging end-users in the AI development process ensures a user-centric design, incorporates valuable feedback, and enables greater adoption by ensuring products are useful to those who need them.

At this Langwatch is already helping - easily viewing messages coming through. It's a period of discovery, understanding, and refining.



πŸŽ‰ Phase 3: Widening the Circle πŸŽ‰

People/users seem to enjoy it, and the company decides to widening the circle and show it to more users. At this point, looking at every message is impossible. So automating the look at messages is important. Plus, questions arise with I don’t know if the AI hallucinating? Are all the outputs relevant and faithful?

What do you do then?

At some point it is too much work to do this manually.

Now you should enable a LangWatch Evaluation > to check the relevancy, and faithfulness of the output of the LLM app automatically for you, while viewing how your users are using the tool, their satisfaction/sentiment of the tool. Creating custom evaluations which are important for you to analyse the quality of your product.

πŸŽ‰ Phase 3: Expanding Horizons πŸŽ‰

As the application begins to resonate with users, the decision to broaden its reach becomes apparent. However, with an expanded user base, manually scrutinizing each message becomes an untenable task. Automating this process becomes imperative, raising questions about the accuracy and relevance of the generated content. How does one ensure the reliability of the outputs? This is where LangWatch Evaluation steps in, automating the assessment of relevancy and faithfulness of the LLM outputs while also gauging user satisfaction. Custom evaluations become a tool not just for analysis but for enhancing product quality.

πŸ“ˆ Phase 5: Scaling and Analytics πŸ“ˆ

With the evaluations setup earlier. You will have more confidence to increase your LLM-app usage. As usage increases to 100 of thousands of messages. The focus shifts from observing through the actual scores to analyzing, from raw scores to insightful LangWatch Analytics being able to share this with upper management or when building solutions for customers, to your customers in actual graphs.

πŸ›‘ Phase 6: Guardrails and Governance πŸ›‘

Scaling from 1000 messages per day > now increases to 10k per day. Then it might get scary. You don’t want to go out and find a jailbreaking user, the scare of "Prompt injections: (The known example of Chevrolet a couple of months ago: ) or a swearing AI-bot  (DPD) - likely the press knows before you know…

You want to "LangWatch Guardrail” it. Making sure you will detect when a certain unsafe instance is happening or even stopping that.

πŸ—οΈ Phase 8: Building for the Future πŸ—οΈ

You go there, it’s scary but it works and you have guardrails in place. Next up: You want to iterate, add more functionalities or you need to move to another LLM to reduce LLM costs. Now before going live with the next version - you want to ensure that the outcomes are at least similar or as good as it was before. Every interaction, every piece of feedback contributes to building a more robust dataset. And you want to be able to test these datasets where LangWatch Datasets helps you with. This iterative process of monitoring, evaluating, and adjusting ensures that with each iteration, confidence grows, and the foundation for future innovation strengthens

Did you reach it till the end of this blogpost? Then it's time to have a call and we will explain you exactly how we can help you launching AI apps with confidence.

Request a demo now.

Last week we spoke with one of our launching customers about how they experience LangWatch at this moment. We came to a great storyline which I’d love to share with you here, so let me walk you through this.

LangWatch goal is to provide a repeatable process that acts as a guiding light for those embarking on the journey of AI integration. This article aims to explore the process, offering insights, strategies, and practical steps that empower decision-makers to leverage the full potential of AI within their organizations.

πŸš€ Phase 1: First Touch πŸš€

It all starts with curiosity. Companies and teams get their hands on LLMs (Large Language Models), playing around, and experimenting. With certain tools and various LLM's out there, they quickly move to create a first proof of concept. Whether this is for internal usage (mostly first) or external use cases - it all start with a first proof of concept.

Start small: This approach is designed to propel organizations forward, enabling them to achieve quick wins through the deployment of modest AI initiatives, including applications like internal Q&A, chatbot technologies, and content generation tools.

Especially beneficial for entities either at the early stages of AI adoption or those operating with limited resources, this strategy offers an good opportunity for experimentation and insight. It acts as a preliminary step, allowing businesses to dip their toes into the vast ocean of AI integration, thereby gaining practical experience and understanding of how AI can be woven into their operational fabric.

πŸ” Phase 2: Testing the Waters πŸ”

Put this proof of concept β€œsomewhere” - at least for internal testing with a first batch of users (employees). See how it behaves, how it works - manually looking at the first couple of messages. Engaging end-users in the development process is crucial to instilling trust in the output generated by the GenAI models. Engaging end-users in the AI development process ensures a user-centric design, incorporates valuable feedback, and enables greater adoption by ensuring products are useful to those who need them.

At this Langwatch is already helping - easily viewing messages coming through. It's a period of discovery, understanding, and refining.



πŸŽ‰ Phase 3: Widening the Circle πŸŽ‰

People/users seem to enjoy it, and the company decides to widening the circle and show it to more users. At this point, looking at every message is impossible. So automating the look at messages is important. Plus, questions arise with I don’t know if the AI hallucinating? Are all the outputs relevant and faithful?

What do you do then?

At some point it is too much work to do this manually.

Now you should enable a LangWatch Evaluation > to check the relevancy, and faithfulness of the output of the LLM app automatically for you, while viewing how your users are using the tool, their satisfaction/sentiment of the tool. Creating custom evaluations which are important for you to analyse the quality of your product.

πŸŽ‰ Phase 3: Expanding Horizons πŸŽ‰

As the application begins to resonate with users, the decision to broaden its reach becomes apparent. However, with an expanded user base, manually scrutinizing each message becomes an untenable task. Automating this process becomes imperative, raising questions about the accuracy and relevance of the generated content. How does one ensure the reliability of the outputs? This is where LangWatch Evaluation steps in, automating the assessment of relevancy and faithfulness of the LLM outputs while also gauging user satisfaction. Custom evaluations become a tool not just for analysis but for enhancing product quality.

πŸ“ˆ Phase 5: Scaling and Analytics πŸ“ˆ

With the evaluations setup earlier. You will have more confidence to increase your LLM-app usage. As usage increases to 100 of thousands of messages. The focus shifts from observing through the actual scores to analyzing, from raw scores to insightful LangWatch Analytics being able to share this with upper management or when building solutions for customers, to your customers in actual graphs.

πŸ›‘ Phase 6: Guardrails and Governance πŸ›‘

Scaling from 1000 messages per day > now increases to 10k per day. Then it might get scary. You don’t want to go out and find a jailbreaking user, the scare of "Prompt injections: (The known example of Chevrolet a couple of months ago: ) or a swearing AI-bot  (DPD) - likely the press knows before you know…

You want to "LangWatch Guardrail” it. Making sure you will detect when a certain unsafe instance is happening or even stopping that.

πŸ—οΈ Phase 8: Building for the Future πŸ—οΈ

You go there, it’s scary but it works and you have guardrails in place. Next up: You want to iterate, add more functionalities or you need to move to another LLM to reduce LLM costs. Now before going live with the next version - you want to ensure that the outcomes are at least similar or as good as it was before. Every interaction, every piece of feedback contributes to building a more robust dataset. And you want to be able to test these datasets where LangWatch Datasets helps you with. This iterative process of monitoring, evaluating, and adjusting ensures that with each iteration, confidence grows, and the foundation for future innovation strengthens

Did you reach it till the end of this blogpost? Then it's time to have a call and we will explain you exactly how we can help you launching AI apps with confidence.

Request a demo now.

Last week we spoke with one of our launching customers about how they experience LangWatch at this moment. We came to a great storyline which I’d love to share with you here, so let me walk you through this.

LangWatch goal is to provide a repeatable process that acts as a guiding light for those embarking on the journey of AI integration. This article aims to explore the process, offering insights, strategies, and practical steps that empower decision-makers to leverage the full potential of AI within their organizations.

πŸš€ Phase 1: First Touch πŸš€

It all starts with curiosity. Companies and teams get their hands on LLMs (Large Language Models), playing around, and experimenting. With certain tools and various LLM's out there, they quickly move to create a first proof of concept. Whether this is for internal usage (mostly first) or external use cases - it all start with a first proof of concept.

Start small: This approach is designed to propel organizations forward, enabling them to achieve quick wins through the deployment of modest AI initiatives, including applications like internal Q&A, chatbot technologies, and content generation tools.

Especially beneficial for entities either at the early stages of AI adoption or those operating with limited resources, this strategy offers an good opportunity for experimentation and insight. It acts as a preliminary step, allowing businesses to dip their toes into the vast ocean of AI integration, thereby gaining practical experience and understanding of how AI can be woven into their operational fabric.

πŸ” Phase 2: Testing the Waters πŸ”

Put this proof of concept β€œsomewhere” - at least for internal testing with a first batch of users (employees). See how it behaves, how it works - manually looking at the first couple of messages. Engaging end-users in the development process is crucial to instilling trust in the output generated by the GenAI models. Engaging end-users in the AI development process ensures a user-centric design, incorporates valuable feedback, and enables greater adoption by ensuring products are useful to those who need them.

At this Langwatch is already helping - easily viewing messages coming through. It's a period of discovery, understanding, and refining.



πŸŽ‰ Phase 3: Widening the Circle πŸŽ‰

People/users seem to enjoy it, and the company decides to widening the circle and show it to more users. At this point, looking at every message is impossible. So automating the look at messages is important. Plus, questions arise with I don’t know if the AI hallucinating? Are all the outputs relevant and faithful?

What do you do then?

At some point it is too much work to do this manually.

Now you should enable a LangWatch Evaluation > to check the relevancy, and faithfulness of the output of the LLM app automatically for you, while viewing how your users are using the tool, their satisfaction/sentiment of the tool. Creating custom evaluations which are important for you to analyse the quality of your product.

πŸŽ‰ Phase 3: Expanding Horizons πŸŽ‰

As the application begins to resonate with users, the decision to broaden its reach becomes apparent. However, with an expanded user base, manually scrutinizing each message becomes an untenable task. Automating this process becomes imperative, raising questions about the accuracy and relevance of the generated content. How does one ensure the reliability of the outputs? This is where LangWatch Evaluation steps in, automating the assessment of relevancy and faithfulness of the LLM outputs while also gauging user satisfaction. Custom evaluations become a tool not just for analysis but for enhancing product quality.

πŸ“ˆ Phase 5: Scaling and Analytics πŸ“ˆ

With the evaluations setup earlier. You will have more confidence to increase your LLM-app usage. As usage increases to 100 of thousands of messages. The focus shifts from observing through the actual scores to analyzing, from raw scores to insightful LangWatch Analytics being able to share this with upper management or when building solutions for customers, to your customers in actual graphs.

πŸ›‘ Phase 6: Guardrails and Governance πŸ›‘

Scaling from 1000 messages per day > now increases to 10k per day. Then it might get scary. You don’t want to go out and find a jailbreaking user, the scare of "Prompt injections: (The known example of Chevrolet a couple of months ago: ) or a swearing AI-bot  (DPD) - likely the press knows before you know…

You want to "LangWatch Guardrail” it. Making sure you will detect when a certain unsafe instance is happening or even stopping that.

πŸ—οΈ Phase 8: Building for the Future πŸ—οΈ

You go there, it’s scary but it works and you have guardrails in place. Next up: You want to iterate, add more functionalities or you need to move to another LLM to reduce LLM costs. Now before going live with the next version - you want to ensure that the outcomes are at least similar or as good as it was before. Every interaction, every piece of feedback contributes to building a more robust dataset. And you want to be able to test these datasets where LangWatch Datasets helps you with. This iterative process of monitoring, evaluating, and adjusting ensures that with each iteration, confidence grows, and the foundation for future innovation strengthens

Did you reach it till the end of this blogpost? Then it's time to have a call and we will explain you exactly how we can help you launching AI apps with confidence.

Request a demo now.