showcasing cool demos that flaunt the potential of Language Models (LLMs). However, the leap from these impressive demonstrations to delivering a reliable, enterprise-ready product has often felt like crossing a chasm.

The gaping chasms faced are dual:

Hallucinations: The tendency of LLMs to hallucinate, fabricating non-existent information and data.  

Consider an LLM-based system designed to handle flight bookings. Instead of booking the flight, it hallucinates and produces a boarding pass. The user, none the wiser, arrives at the airport, only to find that the flight on their boarding pass is non-existent. It’s a glaring example of an LLM creating a facade of a completed task, while in reality, nothing was accomplished.

Unreliability: Unpredictable responses to identical prompts by LLMs, an inconsistency that undermines integration into the current tech ecosystem that thrives on reliability.

Imagine an LLM-based online ticket booking system that's expected to consistently book flights to New York. However, at times, due to inconsistent responses to the same prompt, it books a flight to San Francisco instead. The alarming part for enterprises is the unpredictability—there's no telling when the system might veer off course, making it a shaky foundation for applications that require unfaltering reliability.

Echoes from the Past

But these challenges are not new.

The hurdles we encounter with LLMs today echo the challenges of the Internet's early days. Transmitting data across the nascent, patchy network presented a series of significant challenges. Transmitting data across this lossy & patchy network was fraught with inconsistencies. Digital packets, which carried vital information, often got lost or misrouted, or arrived out of order. The early Internet's unreliability posed a daunting question: How could we build trust in such an unpredictable system?

The Systems Approach

The systems approach posits that breaking down complex problems into simpler units can lead to a deeper understanding. When applied to the challenge of connecting lossy and lossless mediums, the systems approach would emphasize understanding each medium (or component) in its entirety and coordinating both mediums to ensure the collective system functions optimally, despite individual component limitations.

In essence, instead of demanding perfection from every component, the focus is to manage imperfections smartly, ensuring the end experience remains uncompromised.

The solution, as we'd discover, was not to eliminate the uncertainty but to manage it ingeniously. And here, the systems approach became the guiding principle. It envisioned the network as a lossy medium and instead of attempting to eliminate the lossy nature of the component, it aimed at managing it effectively to approximate a lossless system. Two major strategies were employed: firstly, by recognizing a loss when it occurred and retransmitting the data (thanks to packet sequencing and acknowledgments), and secondly, by embedding extra data within each packet to correct errors (through Forward Error Correction and Backward Error Correction).

Fast Forward to Today

If we take inspiration from the past, the problem becomes simpler.

Instead of producing an LLM that is 100% accurate (an impossibility in my view), you just treat it as a lossy medium. And then, when integrated with current tech, manage it the same way lossy mediums have been managed in all the current robust systems (like internet, GPS).

At TrueReach, we've adopted the systems approach, treating LLMs as a lossy medium.

We have employed a unique pre-and-post Processing Middleware to add more context, identify errors, rectify minor ones, and restart the process when necessary, ensuring that the insights generated are always 100% accurate.

The essence of innovation at TrueReach lies in the blend of historical learning with modern-day technology to address contemporary challenges. It isn't merely a product; it's a testament to the potential of a systems-based approach.

Our BI solution, with its ease of use and intuitive natural language interface, effortlessly integrates with the user's current workflows. The personalized dashboards further reduce the learning curve for each employee,.

This ensures that relevance in the insights is not only provided but are used in decision making to drive businesses forward in the modern data-centric landscape.

About TrueReach

TrueReach is a gen-AI native BI and analytics solution. Unlike traditional BIs, ease of deployment & ease of use are at the heart of the solution. The goal is to not just provide charts & reports, but workflows that intuitively enable users to utilize insights from a BI solution to make data-driven decisions.

TrueReach seamlessly integrates with all company data sources ensuring comprehensive connectivity. It automatically creates personalized dashboards for each user based on their monthly goals and KRAs, providing real-time insights into KPIs that matter to them. The user can also do any data analysis, like root-cause analysis, slice-and-dice data, and projections, in real-time, by asking questions in natural language. Importantly, data security is upheld as data does not get transmitted to LLMs. The best part is that the solution can be deployed companywide in just 4-6 weeks.