The Future of Multimodal Search: Beyond Keyword and Vector Hybrid Search!

The Future of Multimodal Search: Beyond Keyword and Vector Hybrid Search!

In today's digital age, the way we search for information is constantly evolving. Traditional keyword-based search engines have dominated the landscape for years, but recent advancements in technology have paved the way for a new era of multimodal search. This innovative approach combines the power of keywords with the intelligence of vector-based search algorithms, creating a more efficient and accurate way to retrieve information. But what does the future hold for multimodal search? Will it surpass the limitations of keywords and vectors? Let's delve deeper into this topic.

One of the key players in the early GPT ecosystem is Jasper. However, there are concerns that it may become marginalized in the face of commercial-grade content generation. Despite its impressive financial performance, with $80 million in ARR and a valuation of $1.5 billion within just 18 months of its establishment, Jasper's profit margin is questioned due to the lack of proprietary models and technological barriers. The user-friendly and cost-effective nature of ChatGPT also poses a significant pricing pressure on Jasper, as it lacks the pricing power in the industry. Additionally, the active embrace of AIGC by platforms like Notion, Office, and Hubspot is likely to rapidly reduce Jasper's market share, closing the window of time to establish scenarios and product barriers.

To overcome the challenges posed by the evolving landscape, Jasper has focused on comprehensive frontend prompts and intricate product design. By addressing the high learning costs and inconvenient usage of early GPT models, and combining them with the team's marketing experience for model refinement, Jasper has effectively leveraged the potential of GPT-3 as an efficiency tool in marketing scenarios. However, the absence of self-developed large-scale models has diminished Jasper's apparent competitive advantage. Website traffic data also indicates a declining penetration rate of Jasper's user base for large language models.

So why do terminal products have the opportunity to thrive? The answer lies in their user-unfriendliness, lack of integration into copywriting workflows, high financial costs of learning, complex model selection, and high prompt thresholds. Template products can guide users to efficiently utilize GPT-3, while document products make it more convenient to invoke GPT-3 within the copywriting process.

Now, let's take a closer look at Jasper. Many customers have reported the need to use multiple templates when writing a complete article, leading to disrupted thinking and workflow. To address this issue, Jasper employs the text-davinci-002 model in Documents, which excels at generating human-preferred answers in zero-shot situations, providing more detailed content and supporting additional instructions. Jasper Chat, on the other hand, is a chatbot based on a series of models such as GPT series, NeoX, T5, and BLOOM. It automatically selects the most suitable model based on user input scenarios and combines it with search engine results to generate accurate content. When it detects marketing-related content, it utilizes its fine-tuned GPT-3 model to deliver higher quality responses compared to ChatGPT. This insight was gained through interviews with customers who expressed a preference for adjusting instructions through conversation rather than constantly tweaking prompts and parameters to achieve desired results.

To remain at the forefront of AI leadership, Jasper has been experimenting with diversified model supply and aggregation in its Chat product. This will be a crucial factor in determining its ability to maintain its position in the market.

In conclusion, the future of multimodal search holds great promise. By combining the strengths of traditional keyword-based search engines with the intelligence of vector-based algorithms, we can expect more accurate and efficient search results. However, to fully realize the potential of multimodal search, it is important to address the challenges faced by terminal products and leverage their unique advantages. Here are three actionable pieces of advice:

1. Enhance user-friendliness: Focus on integrating terminal products into existing workflows and providing intuitive interfaces for users to interact with AI-powered search systems.

2. Reduce learning costs: Invest in comprehensive training programs and resources to help users become proficient in giving instructions to AI models.

3. Simplify model selection and prompt thresholds: Streamline the process of selecting the most appropriate model and provide clear guidelines on setting prompts to ensure optimal results.

By following these recommendations, we can unlock the full potential of multimodal search and revolutionize the way we access and retrieve information in the digital age.