How Does LangChain Enhance The Use Of LLMs?

It is increasingly evident in the field of generative AI app development that incorporating large language models (LLMs) with available contextual data is essential to enhancing the relevance and accuracy of the replies provided. quick ways to enhance AI applications by using LangChain and Tecton to include current context obtained from feature pipelines.

How Does LangChain Enhance The Use Of LLMs

Context’s Power for AI

Even though LLMs perform well on generic tasks, when given pertinent and up-to-date context, their replies may be significantly enhanced. AI models may now transcend their intrinsic limits of static knowledge and lack of real-time information thanks to this contextual improvement. LLMs may provide more precise, nuanced, and customized replies by adding current facts, domain-specific knowledge, or user-relevant data.

Initially Using a Simple LangChain Model

I began by creating a basic LangChain application with the GPT-4o-mini model from OpenAI:

model = ChatOpenAI(model="gpt-4o-mini")

prompt = ChatPromptTemplate.from_template(
    """You are a concierge service that recommends restaurants. 
        Respond to the user query about dining. 
        If the user asks for a restaurant recommendation, respond with a specific restaurant that you know, and suggest menu items. 
        User query:{user_query}""")

chain = prompt | model | StrOutputParser()

I put my approach to the test by asking a user to provide the time and place as well as the rationale behind the recommendation:

inputs = {"user_query":"suggest a restaurant for tonight in Ballantyne area of Charlotte and tell me why you suggest it"}
chain.invoke(inputs).splitlines()

Utilizing Tecton’s Feature Platform to Improve the Model

I then put the model to the test by enhancing it with Tecton’s feature platform, which provides the most recent user context from feature pipelines to the LLM prompt. I updated the prompt with the user’s rating summary by cuisine ({cuisines}) and provided use guidelines for the data:

personalized_prompt = ChatPromptTemplate.from_template(
    """You are a concierge service that recommends restaurants. 
        Respond to the user query about dining. 
        If the user asks for a restaurant recommendation, respond with a specific restaurant that you know and suggest menu items. 
        Respond to the user query by taking into account the user's dining history. 
        Show their rating of the cuisine you recommend.
        If the user does not provide a cuisine, choose a restaurant that fits a cuisine from their highest average ratings:
        User's dining history by cuisine: {cuisines}
        User query:{user_query}""")

personalized_chain = personalized_prompt | model | StrOutputParser()