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15:["$","div",null,{"className":"lg:w-[60%] sm:[95%] md:[75%] mx-auto","children":[["$","$Ld",null,{"className":"inline-flex items-center justify-center whitespace-nowrap rounded-md text-sm font-medium ring-offset-background transition-colors focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:pointer-events-none disabled:opacity-50 text-primary underline-offset-4 hover:underline h-10 px-4 py-2 !mx-0 !px-0 mb-7 !-ml-1 ","href":"/blogs","children":[["$","svg",null,{"ref":"$undefined","xmlns":"http://www.w3.org/2000/svg","width":24,"height":24,"viewBox":"0 0 24 24","fill":"none","stroke":"currentColor","strokeWidth":2,"strokeLinecap":"round","strokeLinejoin":"round","className":"lucide lucide-arrow-left w-4 h-4 mr-1.5","children":[["$","path","1l729n",{"d":"m12 19-7-7 7-7"}],["$","path","x3x0zl",{"d":"M19 12H5"}],"$undefined"]}]," Back to blog"]}],["$","div",null,{"className":"flex flex-col gap-3 pb-7 w-full mb-2","children":[["$","p",null,{"className":"text-muted-foreground text-sm","children":"Thursday, July 10, 2025"}],["$","h1",null,{"className":"sm:text-3xl text-2xl font-extrabold","children":"Why Optimizing Compound AI Systems Matters"}],["$","div",null,{"className":"mt-6 flex flex-col gap-3","children":[["$","p",null,{"className":"text-sm text-muted-foreground","children":"Posted by"}],["$","div",null,{"className":"flex items-center gap-8 flex-wrap","children":[["$","$Ld","Aaron Lee",{"href":"https://www.linkedin.com/in/aaron-lee-076350214/","className":"flex items-center gap-2","children":[["$","$L1d",null,{"className":"w-10 h-10","children":[["$","$L1e",null,{"src":"/aaron-lee.jpg"}],["$","$L1f",null,{"children":"AA"}]]}],["$","div",null,{"className":"","children":[["$","p",null,{"className":"text-sm font-medium","children":"Aaron Lee"}],["$","p",null,{"className":"font-code text-[13px] text-muted-foreground","children":["@","aaron"]}]]}]]}],["$","$Ld","Shiyu Zhao",{"href":"https://www.linkedin.com/in/shiyu-zhao-1124a0266/","className":"flex items-center gap-2","children":[["$","$L1d",null,{"className":"w-10 h-10","children":[["$","$L1e",null,{"src":"/shiyu-zhao.jpg"}],["$","$L1f",null,{"children":"SH"}]]}],["$","div",null,{"className":"","children":[["$","p",null,{"className":"text-sm font-medium","children":"Shiyu Zhao"}],["$","p",null,{"className":"font-code text-[13px] text-muted-foreground","children":["@","shiyu"]}]]}]]}]]}]]}]]}],["$","div",null,{"className":"!w-full","children":[["$","div",null,{"className":"w-full mb-7","children":["$","$L20",null,{"src":"https://img.freepik.com/premium-vector/many-monsters-various-colors-doodle-come-bless-birthday-happy_577083-85.jpg?w=826","alt":"cover","width":700,"height":400,"className":"w-full h-[400px] rounded-md border object-cover"}]}],["$","div",null,{"className":"prose sm:prose-base prose-sm prose-zinc dark:prose-invert prose-code:font-normal prose-code:font-code dark:prose-code:bg-stone-900/25 prose-code:bg-stone-50 prose-pre:bg-background prose-headings:scroll-m-20 w-[85vw] sm:w-full sm:mx-auto prose-code:text-sm prose-code:leading-6 dark:prose-code:text-white prose-code:text-stone-800 prose-code:p-[0.085rem]  prose-code:rounded-md prose-code:border pt-2 !min-w-full prose-img:rounded-md prose-img:border prose-code:before:content-none prose-code:after:content-none prose-code:px-1.5 prose-code:overflow-x-auto !max-w-[500px] prose-img:my-3 prose-h2:my-4 prose-h2:mt-8 prose-code:break-all md:prose-code:break-normal  prose-td:px-4","children":[["$","p",null,{"children":["Machine learning adoption is witnessing a ",["$","strong",null,{"children":"paradigm shift"}]," less reliant on massive monolithic models towards more ",["$","strong",null,{"children":"compound systems"}]," with multiple specialized components wired together into a pipeline towards their goal. Whether it be personalized product recommendations, tested code generation, medical decision support, or any type of multi-hop question-answering, some tasks might simply be easier to accomplish by ",["$","strong",null,{"children":"clever pipeline design"}]," than by scaling up one model."]}],"\n",["$","p",null,{"children":["These chained orchestrations aim to leverage an ensemble of external tools, information retrievers, and calls to foundation models such that they can ",["$","strong",null,{"children":"outperform the sum of their parts"}],"."]}],"\n",["$","h2",null,{"id":"the-movie-production-analogy","children":[["$","$Ld",null,{"href":"#the-movie-production-analogy","aria-hidden":"true","tabIndex":"-1","children":["$","span",null,{"className":"icon icon-link"}],"target":"_blank","rel":"noopener noreferrer"}],"The Movie-Production Analogy"]}],"\n",["$","p",null,{"children":"Imagine our AI’s task as a film shoot with crew: Screenwriter, Casting Director, Cinematographer, Director. They chase the same global target — a film that breaks the box office — yet each owns a different local task. These jobs are tightly linked: if the writer writes a medieval story, the casting director hunts actors who fit that mood, and the cinematographer must change filters to soften modern sharpness; budget and audience taste run through every choice. Because of this coupling, a “perfect” casting that ignores cost or script fit can still hurt the final movie. In short, local success is hard to judge without seeing the rest of the pipeline."}],"\n",["$","blockquote",null,{"children":["\n",["$","p",null,{"children":"This synergy is the thesis for compound AI systems."}],"\n"]}],"\n",["$","h2",null,{"id":"the-challenge-optimization-without-shared-gradients","children":[["$","$Ld",null,{"href":"#the-challenge-optimization-without-shared-gradients","aria-hidden":"true","tabIndex":"-1","children":["$","span",null,{"className":"icon icon-link"}],"target":"_blank","rel":"noopener noreferrer"}],"The Challenge: Optimization Without Shared Gradients"]}],"\n",["$","p",null,{"children":["However, the development of and interaction between our modules, whether they be a parser, retriever, reasoner, or verifier, introduce ",["$","strong",null,{"children":"unique challenges"}]," in completing complex goals. Compound systems do let us:"]}],"\n",["$","ul",null,{"children":["\n",["$","li",null,{"children":"Connect to outside data sources"}],"\n",["$","li",null,{"children":"Enforce access controls"}],"\n",["$","li",null,{"children":"Monitor intermediate steps"}],"\n",["$","li",null,{"children":"Balance cost with quality at a more granular level"}],"\n"]}],"\n",["$","p",null,{"children":["Yet improving the pipeline end-to-end is ",["$","strong",null,{"children":"inherently tricky"}],". Each component comes with its own settings, system prompts, numeric hyperparameters, discrete model choices, even raw model weights, with ",["$","strong",null,{"children":"no shared gradient"}]," to guide optimization. That makes holistic optimization slow, expensive, and turbulent if we have to rerun the entire pipeline for every minor modification."]}],"\n",["$","h2",null,{"id":"enter-optimas-giving-every-role-a-goal-that-knows-the-whole-film","children":[["$","$Ld",null,{"href":"#enter-optimas-giving-every-role-a-goal-that-knows-the-whole-film","aria-hidden":"true","tabIndex":"-1","children":["$","span",null,{"className":"icon icon-link"}],"target":"_blank","rel":"noopener noreferrer"}],"Enter OPTIMAS: Giving every role a goal that knows the whole film"]}],"\n",["$","p",null,{"children":["This is where ",["$","strong",null,{"children":"OPTIMAS"}]," comes in. OPTIMAS works like an on-set producer who watches the daily rushes, early test-screen scores, and budget sheets, then sends each department a local score already adjusted by how last night’s tweak moved total audience buzz and profit forecasts. The casting director learns not only “Did the new actor test well?” but also “Did they raise the director’s re-shoot cost?”; the cinematographer sees whether bolder lighting lifted viewer emotion or confused them. With this global-aware feedback each module—parser, retriever, reasoner, verifier—can update fast on its own while still pushing toward the common win. That is how OPTIMAS turns a set of separate AI components into one well-rehearsed crew ready for a hit premiere."]}],"\n",["$","p",null,{"children":"By optimizing components independently and in lockstep with the global goal, OPTIMAS:"}],"\n",["$","ul",null,{"children":["\n",["$","li",null,{"children":["$","strong",null,{"children":"Shrinks compute costs"}]}],"\n",["$","li",null,{"children":["$","strong",null,{"children":"Accelerates inference"}]}],"\n",["$","li",null,{"children":["$","strong",null,{"children":"Keeps every component pulling in the same direction"}]}],"\n"]}],"\n",["$","h2",null,{"id":"the-future-is-collaborative","children":[["$","$Ld",null,{"href":"#the-future-is-collaborative","aria-hidden":"true","tabIndex":"-1","children":["$","span",null,{"className":"icon icon-link"}],"target":"_blank","rel":"noopener noreferrer"}],"The Future is Collaborative"]}],"\n",["$","p",null,{"children":["In the end, AI isn't about one mega-model handling everything but about ",["$","strong",null,{"children":"assembling a smarter team"}],". The future of machine learning lies not in building bigger models, but in orchestrating specialized components that work together seamlessly towards a common goal."]}],"\n",["$","hr",null,{}],"\n",["$","p",null,{"children":["$","em",null,{"children":"The compound AI approach represents a fundamental shift in how we think about artificial intelligence—from monolithic powerhouses to collaborative ecosystems where the whole truly becomes greater than the sum of its parts."}]}]]}]]}]]}]
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