{"id":18434,"date":"2025-10-25T14:05:43","date_gmt":"2025-10-25T14:05:43","guid":{"rendered":"https:\/\/goteech.io\/?p=18434"},"modified":"2025-11-11T07:17:29","modified_gmt":"2025-11-11T07:17:29","slug":"rag-vs-prompt-chaining-which-to-use","status":"publish","type":"post","link":"https:\/\/goteech.io\/zh-hk\/blog\/learn\/rag-vs-prompt-chaining-which-to-use\/","title":{"rendered":"RAG vs Prompt Chaining: Which to Use?"},"content":{"rendered":"<div data-elementor-type=\"wp-post\" data-elementor-id=\"18434\" class=\"elementor elementor-18434\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-cbfad5b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"cbfad5b\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-85a16e6\" data-id=\"85a16e6\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-eb7eb02 elementor-toc--minimized-on-desktop elementor-widget elementor-widget-table-of-contents\" data-id=\"eb7eb02\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;exclude_headings_by_selector&quot;:&quot;post-recommend, post-recommend-grid&quot;,&quot;marker_view&quot;:&quot;bullets&quot;,&quot;icon&quot;:{&quot;value&quot;:&quot;far fa-circle&quot;,&quot;library&quot;:&quot;fa-regular&quot;},&quot;no_headings_message&quot;:&quot;No headings were found on this page.&quot;,&quot;_animation&quot;:&quot;none&quot;,&quot;minimized_on&quot;:&quot;desktop&quot;,&quot;headings_by_tags&quot;:[&quot;h4&quot;],&quot;minimize_box&quot;:&quot;yes&quot;,&quot;hierarchical_view&quot;:&quot;yes&quot;,&quot;min_height&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;min_height_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;min_height_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"table-of-contents.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-toc__header\">\n\t\t\t\t\t\t<h4 class=\"elementor-toc__header-title\">\n\t\t\t\t\u5167\u5bb9\u76ee\u9304\t\t\t<\/h4>\n\t\t\t\t\t\t\t\t\t\t<div class=\"elementor-toc__toggle-button elementor-toc__toggle-button--expand\" role=\"button\" tabindex=\"0\" aria-controls=\"elementor-toc__eb7eb02\" aria-expanded=\"true\" aria-label=\"Open table of contents\"><i aria-hidden=\"true\" class=\"fas fa-chevron-down\"><\/i><\/div>\n\t\t\t\t<div class=\"elementor-toc__toggle-button elementor-toc__toggle-button--collapse\" role=\"button\" tabindex=\"0\" aria-controls=\"elementor-toc__eb7eb02\" aria-expanded=\"true\" aria-label=\"Close table of contents\"><i aria-hidden=\"true\" class=\"fas fa-chevron-up\"><\/i><\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<div id=\"elementor-toc__eb7eb02\" class=\"elementor-toc__body\">\n\t\t\t<div class=\"elementor-toc__spinner-container\">\n\t\t\t\t<i class=\"elementor-toc__spinner eicon-animation-spin eicon-loading\" aria-hidden=\"true\"><\/i>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-adb21d2 elementor-widget elementor-widget-spacer\" data-id=\"adb21d2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\n\t\t<div class=\"elementor-element elementor-element-89c4ab8 elementor-widget elementor-widget-wp-widget-text\" data-id=\"89c4ab8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><p>Picking how your LLM accesses knowledge is one of the earliest and most important design choices you\u2019ll make for an LLM application. The decision affects accuracy, latency, cost, security, and maintainability. Below I walk through the three mainstream approaches \u2014 Retrieval-Augmented Generation (RAG), prompt chaining, and retrieval-free (parametric) methods \u2014 explain where each shines, and offer practical rules of thumb and hybrid patterns many teams use in production.<\/p>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c410a2a elementor-widget elementor-widget-wp-widget-text\" data-id=\"c410a2a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\">Short definitions: quick view<\/h4>\n<p>RAG (Retrieval-Augmented Generation):<\/b> The system retrieves relevant passages from a vector store (or search index) and conditions the generator on those passages to produce grounded answers. RAG was formalized in the literature and has proven effective on knowledge-intensive tasks.<\/p>\n<p><b>Prompt chaining:<\/b> The app splits a complex task into ordered steps (prompts) where each step\u2019s output feeds the next. It\u2019s useful for multi-step reasoning or structured extraction. Tools like LangChain make this pattern straightforward to implement.<\/p>\n<p><b>Retrieval-free (parametric):<\/b> The model relies only on what it \u201cknows\u201d inside its parameters (possibly via fine-tuning). It\u2019s simple and low-latency but risks hallucination on niche or recent facts. Recent surveys compare retrieval vs. generation methods and show hybrid wins for many knowledge tasks.<\/p>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-571efac elementor-widget elementor-widget-wp-widget-text\" data-id=\"571efac\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\">Why the choice matters<\/h4>\n<p>Accuracy and factual grounding differ dramatically between patterns. RAG supplies explicit evidence that reduces hallucinations, making it the go-to for enterprise knowledge bases and domains that require traceable sources. Multiple surveys and recent engineering guides show RAG consistently improves factuality on knowledge-heavy tasks.<\/p>\n<p>But RAG adds infrastructure: a vector DB, embedding pipelines, security\/permissions and extra latency. Prompt chaining reduces the need to fetch external docs for every call, and retrieval-free flows are simplest to run at scale\u2014but cost you reliability when facts shift or are obscure.<\/p>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e46ac08 elementor-widget elementor-widget-wp-widget-text\" data-id=\"e46ac08\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\"><strong>Compare: accuracy, latency, cost, security<\/strong><\/h4>\n<div class=\"table-scroll\">\n<table class=\"wide-table\" style=\"border-collapse: separate;border-spacing: 0;border-radius: 10px;width: 100%\">\n<tbody>\n<tr style=\"background-color: #0077cb;color: #ffffff\">\n<th style=\"width: 20%\">Approach<\/th>\n<th style=\"width: 30%\">Best for<\/th>\n<th style=\"width: 50%\">Pros \/ Cons<\/th>\n<\/tr>\n<tr>\n<td><b>RAG<\/b><\/td>\n<td>Domain knowledge, documents that change frequently, need for citations<\/td>\n<td><b>Pros:<\/b> factual grounding, auditable outputs; <b>Cons:<\/b> vector DB ops, higher latency, security controls required.<\/td>\n<\/tr>\n<tr>\n<td><b>Prompt chaining<\/b><\/td>\n<td>Complex multi-step tasks, structured reasoning, extraction workflows<\/td>\n<td><b>Pros:<\/b> better control and debuggability; <b>Cons:<\/b> orchestration complexity, can be slower with many steps.<\/td>\n<\/tr>\n<tr>\n<td><b>Retrieval-free<\/b><\/td>\n<td>Stable knowledge, low-latency interfaces, cost-sensitive endpoints<\/td>\n<td><b>Pros:<\/b> simple infra, lower latency; <b>Cons:<\/b> higher hallucination risk, hard to update facts without retraining.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9374270 elementor-hidden-desktop elementor-widget elementor-widget-spacer\" data-id=\"9374270\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4ce106e elementor-widget elementor-widget-wp-widget-text\" data-id=\"4ce106e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\"><strong>Practical decision guide<\/strong><\/h4>\n<p><b>Need auditable facts or citations? Use RAG.<\/b> If your users expect sources (legal, medical, internal knowledge), RAG provides the explicit evidence the model can ground answers in. The original RAG research and subsequent surveys find large factuality gains for knowledge-intensive QA when retrieval is used.<\/p>\n<p><b>Need stepwise reasoning or structured outputs? Use prompt chaining.<\/b> For workflows that require decomposition (e.g., \u201cextract entities \u2192 normalize \u2192 summarize\u201d), chaining gives you modularity and easier debugging. LangChain and IBM tutorials demonstrate common chaining patterns for production tasks.<\/p>\n<p><b>Require very low latency and simple infra? Consider retrieval-free.<\/b> For short UI prompts or frequently used, stable knowledge, relying on the model (or fine-tuning) avoids retrieval overhead. But add human checks for edge cases.<\/p>\n<p><b>Worried about centralized vector DB security?<\/b> Recent security research and industry reporting flag vector database vulnerabilities and data governance issues; enterprises sometimes prefer agent\/connector patterns that preserve source-level access controls. If compliance is central, evaluate hardened RAG deployments or on-demand retrieval architectures.<\/p>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-983069e elementor-widget elementor-widget-wp-widget-text\" data-id=\"983069e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\"><strong>Hybrid patterns that work in production<\/strong><\/h4>\n<p>Most teams don\u2019t pick a single strategy forever \u2014 they combine patterns to balance trade-offs:<\/p>\n<ul>\n<li><b>Cascade \/ cheap-first:<\/b> Try a fast retrieval-free or small model first; if confidence is low, escalate to RAG or a larger model. This controls cost and latency.<\/li>\n<li><b>Chain + targeted retrieval:<\/b> Use prompt chaining for workflow control but only call retrieval for the steps that need factual grounding.<\/li>\n<li><b>Cache-augmented RAG:<\/b> Cache common retrievals or final answers to reduce repeated vector lookups and improve latency for frequent queries.<\/li>\n<\/ul>\n<p>Microsoft and other engineering teams document these hybrid approaches as practical, production-friendly tradeoffs.<\/p>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e1a4443 elementor-widget elementor-widget-wp-widget-text\" data-id=\"e1a4443\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\"><strong>Implementation checklist<\/strong><\/h4>\n<ul>\n<li>Prepare a representative evaluation set that mirrors real queries.<\/li>\n<li>Choose diverse retrieval strategies and prompts; test each approach on the same testbed.<\/li>\n<li>Measure more than accuracy: track latency, cost per query, calibration\/confidence, and security posture.<\/li>\n<li>Add human fallback for low-confidence or high-impact cases.<\/li>\n<li>Instrument logs for provenance: store which docs were retrieved, model versions, and confidence scores for audits.<\/li>\n<\/ul>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3836bcc elementor-widget elementor-widget-wp-widget-text\" data-id=\"3836bcc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\"><strong>Security &amp; governance considerations<\/strong><\/h4>\n<p>RAG centralizes knowledge into a vector index which may bypass original access controls and expand your attack surface. OWASP and recent enterprise writeups highlight embedding\/ vector vulnerabilities, poisoning risks, and data leakage concerns if indexes are not properly isolated and access-controlled. If you handle sensitive data, take steps such as encryption at rest, strict RBAC, query redaction, and per-request access checks \u2014 or consider runtime retrieval patterns that preserve source-level permissions.<\/p>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-899576d elementor-widget elementor-widget-wp-widget-text\" data-id=\"899576d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\"><strong>Final thought<\/strong><\/h4>\n<p>There\u2019s no one-size-fits-all answer. Start with simple experiments: implement a small RAG prototype and a prompt-chain for the same task, measure accuracy, latency, and cost, then pick a hybrid that fits your constraints. For product teams building aggregators or knowledge apps, mastering these trade-offs is how you deliver accurate, cost-effective, and secure experiences.<\/p>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5126776 elementor-widget elementor-widget-wp-widget-text\" data-id=\"5126776\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\"><strong>Frequently Asked Questions<\/strong><\/h4>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d3f3091 arrow-right accordions-style-2 elementor-widget elementor-widget-mae-accordions\" data-id=\"d3f3091\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;none&quot;,&quot;_animation_delay&quot;:400}\" data-widget_type=\"mae-accordions.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\n\t\t<div class=\"master-accordions\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"item elementor-repeater-item-6af5a5f active\">\n\t\t\t\t\t<div class=\"title clearfix\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"arrow\">\n\t\t\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"unic unic-angle-down\"><\/i>\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\n\t\t\t\t\t\t<h3>Will RAG completely eliminate hallucination?<\/h3>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div class=\"content\">\n\t\t\t\t\t\tNo. RAG reduces hallucinations by providing context, but hallucination can still occur when retrieved passages are incomplete, misranked, or inconsistent. Combine retrieval quality checks and human review for high-stakes tasks.\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"item elementor-repeater-item-4af04f8\">\n\t\t\t\t\t<div class=\"title clearfix\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"arrow\">\n\t\t\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"unic unic-angle-down\"><\/i>\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\n\t\t\t\t\t\t<h3>Is prompt chaining just a development trick or production-ready?<\/h3>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div class=\"content\">\n\t\t\t\t\t\tIt\u2019s production-ready and widely used. Prompt chaining improves control and debugging, but it requires orchestration (state handling, retries), which frameworks such as LangChain simplify.\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"item elementor-repeater-item-b08f30f\">\n\t\t\t\t\t<div class=\"title clearfix\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"arrow\">\n\t\t\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"unic unic-angle-down\"><\/i>\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\n\t\t\t\t\t\t<h3>Can I mix retrieval-free and RAG?<\/h3>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div class=\"content\">\n\t\t\t\t\t\tYes \u2014 many systems use a retrieval-free pass for straightforward queries and escalate to RAG for ambiguous or evidence-required queries (cascade pattern).\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t    <\/div>\n\n\t    \t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d5b7f9c elementor-widget elementor-widget-spacer\" data-id=\"d5b7f9c\" 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class=\"elementor-screen-only\">Facebook-f<\/span>\n\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"fab fa-facebook-f\"><\/i>\t\t\t\t\t<\/a>\n\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<span class=\"elementor-grid-item\" role=\"listitem\">\n\t\t\t\t\t<a class=\"elementor-icon elementor-social-icon elementor-social-icon-x-twitter elementor-repeater-item-c81668c\" href=\"http:\/\/x.com\/\" target=\"_blank\">\n\t\t\t\t\t\t<span class=\"elementor-screen-only\">X-twitter<\/span>\n\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"fab fa-x-twitter\"><\/i>\t\t\t\t\t<\/a>\n\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<span class=\"elementor-grid-item\" role=\"listitem\">\n\t\t\t\t\t<a class=\"elementor-icon elementor-social-icon elementor-social-icon-linkedin-in elementor-repeater-item-c1bfed6\" href=\"https:\/\/www.linkedin.com\" target=\"_blank\">\n\t\t\t\t\t\t<span class=\"elementor-screen-only\">Linkedin-in<\/span>\n\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"fab fa-linkedin-in\"><\/i>\t\t\t\t\t<\/a>\n\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<span class=\"elementor-grid-item\" role=\"listitem\">\n\t\t\t\t\t<a class=\"elementor-icon elementor-social-icon elementor-social-icon-whatsapp elementor-repeater-item-609b641\" href=\"https:\/\/web.whatsapp.com\/\" target=\"_blank\">\n\t\t\t\t\t\t<span class=\"elementor-screen-only\">Whatsapp<\/span>\n\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"fab fa-whatsapp\"><\/i>\t\t\t\t\t<\/a>\n\t\t\t\t<\/span>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fb8fcab elementor-widget elementor-widget-spacer\" data-id=\"fb8fcab\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9482c13 align--mobileleft animated-fast align-left elementor-invisible elementor-widget elementor-widget-mae-link\" data-id=\"9482c13\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;fadeInRight&quot;,&quot;_animation_delay&quot;:200}\" data-widget_type=\"mae-link.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\n        <a class=\"master-link  icon-left\" href=\"https:\/\/goteech.io\/zh-hk\/resources\/\" >\n            <span class=\"icon unic unic-arrow-circle-left\"><\/span>            <span>\u8fd4\u56de\u60a8\u7684\u8cc7\u6e90<\/span>\n                    <\/a>\n\n        \t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-66725a6 elementor-widget elementor-widget-spacer\" data-id=\"66725a6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"<p>\u4e0d\u518d\u6191\u76f4\u89ba\u6c7a\u5b9a\u6aa2\u7d22\u7b56\u7565\u3002\u672c\u6307\u5357\u8aaa\u660e\u4f55\u6642\u9078\u7528 RAG\u3001Prompt Chaining \u6216\u7121\u6aa2\u7d22\u65b9\u5f0f\uff0c\u5404\u81ea\u53d6\u6368\uff0c\u4ee5\u53ca\u5be6\u52d9\u4e0a\u4e0b\u4e00\u6b65\u61c9\u5982\u4f55\u8a2d\u8a08\u3002<\/p>","protected":false},"author":2,"featured_media":18642,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[97],"tags":[],"class_list":["post-18434","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-learn"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/posts\/18434","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/comments?post=18434"}],"version-history":[{"count":6,"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/posts\/18434\/revisions"}],"predecessor-version":[{"id":18634,"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/posts\/18434\/revisions\/18634"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/media\/18642"}],"wp:attachment":[{"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/media?parent=18434"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/categories?post=18434"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/tags?post=18434"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}