How JavaScript Rendering Works and How to Use It Strategically

If you can put in the additional
legwork, server-side rendering plus HTML caching
can significantly reduce server render time. The upside to server-side rendering
is the ability to pull more “live” data and respond to a more complete set of
requests than is possible with static rendering. Pages that need personalization
are a concrete example of the type of request that doesn’t work well with static
rendering. Because of this, radiosity is a prime component of leading real-time rendering methods, and has been used from beginning-to-end to create a large number of well-known recent feature-length animated 3D-cartoon films.

what is rendering in programming

However, when advanced radiosity estimation is coupled with a high quality ray tracing algorithm, images may exhibit convincing realism, particularly for indoor scenes. The older form of rasterization is characterized by rendering an entire face (primitive) as a single color. Alternatively, rasterization can be done in a more complicated manner by first rendering the vertices of a face and then rendering the pixels of that face as a blending of the vertex colors.


This can help solve the problem of fitting images into displays, and, furthermore, suggest what short-cuts could be used in the rendering simulation, since certain subtleties will not be noticeable. Choosing how to render a scene usually involves a trade-off between speed and realism (although realism is not always desired). The techniques developed over the years follow a loose progression, with more advanced what is rendering methods becoming practical as computing power and memory capacity increased. Many rendering algorithms have been researched, and software used for rendering may employ a number of different techniques to obtain a final image. A rendered image can be understood in terms of a number of visible features. Rendering research and development has been largely motivated by finding ways to simulate these efficiently.

what is rendering in programming

This newer method of rasterization utilizes the graphics card’s more taxing shading functions and still achieves better performance because the simpler textures stored in memory use less space. Sometimes designers will use one rasterization method on some faces and the other method on others based on the angle at which that face meets other joined faces, thus increasing speed and not hurting the overall effect. If a pixel-by-pixel (image order) approach to rendering is impractical or too slow for some task, then a primitive-by-primitive (object order) approach to rendering may prove useful. Here, one loop through each of the primitives, determines which pixels in the image it affects, and modifies those pixels accordingly. This is called rasterization, and is the rendering method used by all current graphics cards.

Stream server-side rendering and rehydrate progressively

Textures are 2D images used in the 3D space to make the objects look better and more realistic. Textures are combined from single texture elements called texels the same way picture elements are combined from pixels. Applying textures onto objects during the fragment processing stage of the rendering pipeline allows us to adjust it by wrapping and filtering it if necessary. The choice between server-side and client-side rendering methods depends on the project requirements, with CSR being more suitable for dynamic, interactive applications, and server-side rendering for static, SEO-sensitive sites. This integration of rendering into the development lifecycle on Sanity is essential for delivering polished, high-performing, and SEO-friendly final products. However, faster initial page load times can impact the factors that tell search engines that a page not only has good content, but also a nice user experience, like Core Web Vitals.

what is rendering in programming

Another potential downside for SSG is that it generates all your pages at build-time using the data that is available at that time. This is great for pages that don’t change often, like blogs or marketing pages. But, for pages that change often, this can lead to displaying outdated data. If you need the latest available data on a page, you’d be better suited with something like SSR that fetches the latest data for each request. But actually, the problem that rendering patterns solve is not only where, but also when HTML/CSS or directly DOM/CSSOM are generated.

How To Start Learning Programming for Schoolchildren: Useful Lessons and Applications

Some relate directly to particular algorithms and techniques, while others are produced together. A software application or component that performs rendering is called a rendering engine,[1] render engine, rendering system, graphics engine, or simply a renderer. Rendering refers to the process of converting raw data into a visual representation that can be displayed on a screen. In the context of JavaScript, rendering involves manipulating the Document Object Model (DOM) to update the content of a web page dynamically. The DOM represents the structure of an HTML document and serves as an interface for interacting with its elements.

  • There’s a long-standing debate over the
    correct applications of server-side rendering versus client-side rendering, but
    you can always choose to use server-side rendering for some pages and not
  • Vertex and fragment processing are programmable — you can write your own shaders that manipulate the output.
  • This is mainly because image displays and human perception have restricted ranges.

Now your code will be rendered by the browser and it’ll show you a cat image on the browser. It’s just the process of producing the results out of the piece of code that we have written either in client side or server side. This website is using a security service to protect itself from online attacks. There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data. Vertex and fragment processing are programmable — you can write your own shaders that manipulate the output.

Understanding JavaScript Rendering: a Beginner Guide

This makes JavaScript rendering more difficult than the interpretation of plain HTML. Without it, pages would be static instead of interactive, still instead of animated. Set up a personal 1-on-1 demo today and let’s see what solution best fits your use case. With these drawbacks, CSR only makes sense if your web projects don’t require SEO.

For experiences with little or
no interactivity, server-side rendering can represent a more scalable solution
to these issues. The primary downside to client-side rendering is that the amount of JavaScript
required tends to grow as an application grows, which can impact a page’s INP. This becomes especially difficult with the addition of new JavaScript libraries,
polyfills, and third-party code, which compete for processing power and must
often be processed before a page’s content can render. Another useful test is to use
network throttling in Chrome DevTools
and see how much JavaScript downloads before a page becomes interactive. Prerendering generally needs more JavaScript to become interactive, and that
JavaScript tends to be more complex than the
progressive enhancement
approach used in static rendering. Running page logic and
rendering on the server lets you avoid sending lots of JavaScript to the client.

For each of these mostly-static parts, the
corresponding JavaScript code is then transformed into inert references and
decorative features, reducing their client-side footprint to nearly zero. The primary downside of server-side rendering with rehydration is that it can
have a significant negative impact on TBT and INP, even if it improves FCP. Server-side rendered pages can appear to be loaded and interactive, but can’t
actually respond to input until the client-side scripts for components are
executed and event handlers have been attached. If you’re unsure whether a given solution is static rendering or prerendering,
try disabling JavaScript and load the page you want to test. For statically
rendered pages, most interactive features still exist without JavaScript. Prerendered pages might still have some basic features like links with
JavaScript disabled, but most of the page is inert.

what is rendering in programming

Navigation requests like full page loads or
reloads are handled by a server that renders the application to HTML, then the
JavaScript and data used for rendering is embedded into the resulting document. When done carefully, this achieves a fast FCP like server-side rendering, then
“picks up” by rendering again on the client. This is an effective solution, but
it can have considerable performance drawbacks. Getting server-side rendering “right” can involve finding or building a solution
for component caching, managing
memory consumption, using memoization techniques,
and other concerns. You’re often processing or rebuilding the same app twice,
once on the client and once on the server. Server-side rendering showing content
sooner doesn’t necessarily give you less work to do.

Chronology of concepts

To help you decide which tool might be right for you based on the rendering method you want to use here are some options to help you get started. The other possible downside with SSR is the need to have a server running to generate the pages on-demand for each user request that comes in. But, while this is something to be aware of, in the present day of web development it’s not something to be concerned about because modern hosting solutions like Vercel and Netlify handle and maintain all the servers for you and your website. It’s also worth mentioning that SSR is inherently more secure because you have the option to run sensitive code on the server as the page is being generated.

what is rendering in programming

What Is Prompt Engineering? Complete Guide + Examples

Cutting-edge techniques such as Chain of Thought Prompting, Self Consistency Prompting, and Tree of Thought Prompting amplify efficiency in generating AI prompts. Unlike humans, LLMs don’t have inherent skills, common sense or the ability to fill in gaps in communication. Understanding the centrality of prompts is key to steering these powerful technologies toward benevolent ends.

what is prompt engineering

Organizations looking to incorporate gen AI tools into their business models can either use off-the-shelf gen AI models or customize an existing model by training it with their own data. Researchers and practitioners leverage generative AI to simulate cyberattacks and design better defense strategies. Additionally, crafting prompts for AI models can aid in discovering vulnerabilities in software. However, by breaking down the problem into two discrete steps and asking the model to solve each one separately, it can reach the right (if weird) answer. Zero-shot chain-of-thought prompting is as simple as adding “explain your reasoning” to the end of any complex prompt. Anna Bernstein, for example, was a freelance writer and historical research assistant before she became a prompt engineer at

Critical thinking

Few-shot prompting plays a vital role in augmenting the performance of extensive language models on intricate tasks by offering demonstrations. However, it exhibits certain constraints when handling specific logical problems, thereby implying the necessity for sophisticated prompt engineering and alternative techniques like chain-of-thought prompting. Significant language models such as GPT-4 have revolutionized the manner in which natural language processing tasks are addressed.

what is prompt engineering

Self-consistency prompting is a sophisticated technique that expands upon the concept of Chain of Thought (CoT) prompting. The primary objective of this methodology is to enhance the naive greedy decoding, a trait of CoT prompting, by sampling a range of diverse reasoning paths and electing the most consistent responses. The use of semantic embeddings in search enables the rapid and efficient acquisition of pertinent information, especially prompt engineering cource in substantial datasets. Semantic search offers several advantages over fine-tuning, such as increased search speeds, decreased computational expenses, and the avoidance of confabulation or the fabrication of facts. Consequently, when the goal is to extract specific knowledge from within a model, semantic search is typically the preferred choice. Unlocking AI systems’ full potential in Prompt Engineering extends beyond mere prompting.

Advantages and Disadvantages of Prompt Engineering

Learn about different AI models, how they are trained, and their applications. AI models are designed to understand and generate human-like text, so a clear, concise question or statement will yield the best results. To effectively utilize the capabilities of the AI model, you need to familiarize yourself with its strengths and limitations. This will enable you to craft prompts that align with the model’s abilities, ensuring more accurate and relevant responses. Utilizing ‘Reflexion’ for iterative refinement of the current implementation facilitates the development of high-confidence solutions for problems where a concrete ground truth is elusive. This approach involves the relaxation of the success criteria to internal test accuracy, thereby empowering the AI agent to solve an array of complex tasks that are currently reliant on human intelligence.

what is prompt engineering

This suggests that prompt engineering as a job (or at least a function within a job) continues to be valuable and won’t be going away any time soon. In the above example, the prompt included specific instructions to respond in a specific manner. But when the user noticed that Bard didn’t follow instructions, they added even more guidance to their prompts. The results speak for themselves, showing how proper guidance can yield meaningful results.

Learn About AWS

Making sure that generative AI services like ChatGPT are able to deliver outputs requires engineers to build code and train the AI on extensive and accurate data. Prompt engineering is rapidly emerging as a critical skill in the age of Artificial Intelligence (AI). As AI continues to revolutionize various fields, prompt engineering empowers us to extract the most value from these powerful models. This comprehensive guide dives deep into the world of prompt engineering, exploring its core principles, applications, and best practices. Some experts question the value of the role longer term, however, as it becomes possible to get better outputs from clumsier prompts. But there are countless use cases for generative tech, and quality standards for AI outputs will keep going up.

Instead of using programming languages, AI prompting uses prose, which means that people should unleash their inner linguistics enthusiast when developing prompts. The researchers used similar prompts to improve performance on other logic, reasoning, and mathematical benchmarks. Self-consistency is an advanced form of chain-of-thought prompting developed by Wang et al. (2002). It involves giving the AI multiple examples of the different kinds of reasoning that will lead it to the correct answer and then selecting the most consistent answer it gives. Prompt engineering is constantly evolving as researchers develop new techniques and strategies. While not all these techniques will work with every LLM—and some get pretty advanced—here are a few of the big methods that every aspiring prompt engineer should be familiar with.

Misconception: All prompt engineers do is type words.

This is especially important for complex topics or domain-specific language, which may be less familiar to the AI. Instead, use simple language and reduce the prompt size to make your question more understandable. This technique involves prompting the model to first generate relevant facts needed to complete the prompt. This often results in higher completion quality as the model is conditioned on relevant facts. For example, if the question is a complex math problem, the model might perform several rollouts, each involving multiple steps of calculations.

what is prompt engineering

The model answers complex questions based on prompts, identifies the source of each answer, and extracts information from pictures and tables. As just one example of the potential power of prompt engineering, let’s look at the banking industry. McKinsey estimates that gen AI tools could create value from increased productivity of up to 4.7 percent of the industry’s annual revenues. Gen AI could enable labor productivity growth of up to 0.6 percent annually through 2040—but that all depends on how fast organizations are able to adopt the technology and effectively redeploy workers’ time. Employees with skills that stand to be automated will need support in learning new skills, and some will need support with changing occupations. Prompt engineering is all about taking a logical approach to creating prompts that guides an AI model into giving you the most correct response possible.

Chain-of-thought (CoT) prompting

You can also phrase the
instruction as a question, or give the model a “role,” as seen in the second
example below. One of the tricks of helping AI models to generate more accurate results is to provide feedback and follow-up instructions by clearly communicating what the AI did right or wrong in its response. Clear objectives – Ensure your prompts clearly state what you ask the AI to do. Ambiguity can lead to irrelevant or broad responses, so be specific about your requirements. Learn how to craft effective AI prompts with practical examples for optimal results. Prompt Engineering has emerged as the linchpin in the evolving human-AI relationship, making communication with technology more natural and intuitive.

A standout feature of these models is their capacity for zero-shot learning, indicating that the models can comprehend and perform tasks without any explicit examples of the required behavior. This discussion will delve into the notion of zero-shot prompting and will include unique instances to demonstrate its potential. Let’s say a large corporate bank wants to build its own applications using gen AI to improve the productivity of relationship managers (RMs).

For specific user input, the models work by predicting the best output that they determine from past training. More Relevant Results – By fine-tuning prompts, you can guide the AI to understand the context better and produce more accurate and relevant responses. Different AI models have different requirements, and by writing good prompts, you can get the best of each model. While the term prompt engineering may seem complex all on its own, it’s actually quite straightforward to understand. The vast majority of people do not necessarily know how to frame a Google search to solve a problem or answer a query. Those that do, meanwhile, often get rewarded handsomely, especially in jobs that require quick decision-making and problem-solving.

  • As AI integrates deeper into our daily lives, the importance of Prompt Engineering in mediating our engagement with technology is undeniable.
  • Experiment with different prompts to see what works best for different applications.
  • Chain of Thought (CoT) prompting encourages the LLM to explain its reasoning.
  • Prompt engineers bridge the gap between your end users and the large language model.
  • By offering examples and tweaking the model’s parameters, fine-tuning allows the model to yield more precise and contextually appropriate responses for specific tasks.
  • Today, Prompt Engineering stands at the forefront of AI development, crucially adapting as new challenges arise.