Lets Build Simple RAG Application

Author(s): Adipta Martulandi

Originally published on Towards AI.

Hands on Implementation RAG Application using Langchain

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Large Language Models (LLMs) have revolutionized the way we interact with technology. Their ability to generate human-like text, answer questions, and process language has unlocked new possibilities across various industries. However, despite their impressive capabilities, LLMs have inherent limitations that can impact their effectiveness in real-world applications.

One major drawback is their inability to access up-to-date or real-time information. Most LLMs are trained on static datasets that reflect the state of knowledge at a specific point in time. This means they cannot provide accurate responses about recent events, emerging trends, or newly published data. For instance, if an LLM was last trained in 2022, it would not be aware of events, advancements, or updates that occurred afterward.

This limitation becomes critical when building applications that rely on current or domain-specific knowledge, such as financial forecasts, research insights, or live market data. In such cases, relying solely on pre-trained LLMs can lead to incomplete or outdated answers, undermining the application’s utility.

According to Whitepaper titled Agents by Google researcher, we can Enhanced our LLMs performance with several techniques:

In-context learning: This approach equips a generalized model with a prompt, tools, and a few… Read the full blog for free on Medium.

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Published via Towards AI