WEBVTT 00:00.120 --> 00:06.360 Basic prompting is often sufficient for simple tasks like summarization or short question and answer 00:06.360 --> 00:07.320 interactions. 00:07.840 --> 00:13.440 However, once you move into production systems, basic prompting quickly falls apart. 00:14.080 --> 00:21.160 Real world applications demand consistent behavior, multi-step reasoning, structured outputs, and 00:21.160 --> 00:25.160 strong constraints that simple prompts cannot reliably enforce. 00:25.840 --> 00:29.200 Advanced prompting techniques exist to close this gap. 00:29.880 --> 00:36.520 They help transform large language models from unpredictable text generators into dependable components 00:36.520 --> 00:37.920 of engineered systems. 00:38.640 --> 00:45.200 These techniques improve reasoning depth, reduce hallucinations, and increase output consistency across 00:45.200 --> 00:46.480 diverse scenarios. 00:47.040 --> 00:53.120 The key mindset shift here is this advanced prompting is not about making prompts clever or verbose. 00:53.560 --> 00:56.120 It is about making model behavior predictable. 00:56.520 --> 01:02.910 When you introduce structured reasoning, multiple validation paths and explicit constraints, you reduce 01:02.910 --> 01:05.790 uncertainty and increase trustworthiness. 01:05.830 --> 01:11.550 This section builds directly on prompt fundamentals and moves into techniques that are essential for 01:11.550 --> 01:12.910 serious applications. 01:13.630 --> 01:19.990 If you are building anything beyond a demo, especially systems that affect users decisions or business 01:19.990 --> 01:23.670 logic, advanced prompting is not optional. 01:23.990 --> 01:26.390 It is a core reliability strategy. 01:26.790 --> 01:34.070 Chain of thought prompting, often abbreviated as Cot, is a technique that encourages the model to 01:34.110 --> 01:37.910 reason step by step before producing a final answer. 01:38.550 --> 01:44.750 Instead of jumping directly to a conclusion, the model is instructed to break the problem down into 01:44.750 --> 01:48.750 logical stages and explain how it arrives at the solution. 01:49.470 --> 01:56.190 This is typically activated with instructions such as explain your reasoning step by step before giving 01:56.190 --> 01:57.270 the final answer. 01:57.790 --> 02:02.990 That single instruction dramatically changes how the model approaches complex problems. 02:03.510 --> 02:09.070 Chain of thought is especially effective for tasks that require reasoning rather than recall. 02:09.870 --> 02:16.710 These include mathematical problems, logic puzzles, multi-step decision making, and analytical workflows. 02:17.270 --> 02:23.830 By explicitly reasoning through intermediate steps, the model becomes more accurate and more transparent. 02:24.070 --> 02:28.590 For engineers, this transparency is extremely valuable. 02:29.070 --> 02:35.310 It allows you to inspect how the model is thinking, debug failures, and understand why a particular 02:35.310 --> 02:36.590 answer was produced. 02:37.230 --> 02:41.030 However, this technique is not meant for every task. 02:41.470 --> 02:45.990 It shines when reasoning matters and clarity is required. 02:46.310 --> 02:47.230 Chain of thought. 02:47.230 --> 02:53.230 Prompting works because it forces structural decomposition of complex problems. 02:53.830 --> 03:00.460 Instead of treating a task as a single pattern matching exercise, the model breaks it into manageable 03:00.460 --> 03:04.100 steps, mirroring how humans typically solve problems. 03:04.620 --> 03:11.100 By tracking intermediate reasoning stages, the model maintains logical consistency throughout the process. 03:11.700 --> 03:18.060 This reduces contradictions, skips steps, and incorrect assumptions that often appear when the model 03:18.060 --> 03:20.420 tries to shortcut directly to an answer. 03:21.220 --> 03:24.180 Another important benefit is pattern avoidance. 03:24.460 --> 03:31.740 Without explicit reasoning, llms may rely on shallow correlations or memorized patterns that look correct 03:31.740 --> 03:33.500 but fail in edge cases. 03:34.060 --> 03:40.420 Chain of thought reduces this behavior by anchoring outputs in step by step logic rather than surface 03:40.460 --> 03:41.580 level similarity. 03:41.860 --> 03:44.660 There is an important trade off to understand. 03:45.060 --> 03:50.940 Chain of thought increases token usage, which raises both latency and cost. 03:51.340 --> 03:57.580 For this reason, it should be reserved for tasks where reasoning quality genuinely matters. 03:58.050 --> 04:05.770 Simple lookups, factual queries and straightforward transformations do not benefit from CBT and should 04:05.770 --> 04:06.450 avoid it. 04:06.810 --> 04:10.850 As with all advanced techniques, intentional use is key. 04:11.050 --> 04:16.810 There are two primary ways to apply chain of thought, prompting explicit and implicit. 04:17.410 --> 04:21.890 Each serves a different purpose and should be used at different stages of development. 04:22.610 --> 04:28.090 Explicit chain of thought asks the model to show its full reasoning process in the output. 04:28.530 --> 04:31.850 This approach is invaluable during development and testing. 04:32.330 --> 04:38.250 It allows engineers to inspect reasoning paths, debug incorrect logic, and validate that the model 04:38.250 --> 04:40.570 is solving problems in the intended way. 04:41.330 --> 04:48.490 However, explicit reasoning can lead to over verbose outputs and may expose internal logic unnecessarily 04:48.490 --> 04:49.610 to end users. 04:50.450 --> 04:53.330 Implicit chain of thought takes a different approach. 04:53.890 --> 04:58.680 The model is instructed to reason internally, but return only the final answer. 04:59.480 --> 05:03.960 This preserves reasoning quality while keeping outputs clean and concise. 05:04.600 --> 05:11.320 Implicit code is ideal for production systems, customer facing applications, and situations where 05:11.320 --> 05:13.840 proprietary logic must be protected. 05:14.160 --> 05:20.800 The engineering best practice is clear use explicit chain of thought during development to validate 05:20.800 --> 05:25.320 behavior, then switch to implicit chain of thought in production. 05:26.040 --> 05:32.680 This balances transparency, performance, and user experience without sacrificing accuracy. 05:32.920 --> 05:38.120 Self-consistency prompting addresses one of the core weaknesses of LMS. 05:38.400 --> 05:39.360 Randomness. 05:40.080 --> 05:46.360 Instead of relying on a single response, Self-consistency generates multiple independent reasoning 05:46.360 --> 05:51.000 paths for the same problem and then compares the results. 05:51.720 --> 05:53.840 The process is simple in concept. 05:54.160 --> 06:01.040 First, the model generates several responses, often with different random seeds or sampling variations. 06:01.440 --> 06:05.680 Next, those outputs are analyzed to identify consensus patterns. 06:06.080 --> 06:09.960 Finally, the most common answer is selected as the final result. 06:10.480 --> 06:17.000 This technique works because incorrect answers tend to vary, while correct answers converge by filtering 06:17.000 --> 06:18.720 out statistical outliers. 06:18.840 --> 06:25.200 Self-consistency significantly increases reliability, especially for complex reasoning tasks. 06:25.680 --> 06:28.320 However, this improvement comes at a cost. 06:28.640 --> 06:35.600 Self-consistency multiplies token usage, increases inference time, and raises operational expenses. 06:36.360 --> 06:42.520 It should be applied selectively and only when the value of increased accuracy justifies the overhead. 06:42.960 --> 06:50.680 Self-consistency is particularly useful in high stakes domains such as finance, medicine, or legal 06:50.680 --> 06:55.200 analysis, where correctness matters more than speed or cost. 06:56.030 --> 07:02.190 Self consistency is a powerful tool, but it is not appropriate for every scenario. 07:02.830 --> 07:07.230 Engineers must decide carefully when the benefits outweigh the costs. 07:07.790 --> 07:11.510 Self-consistency should be used for high stakes decisions. 07:11.630 --> 07:17.070 Complex reasoning problems and situations where errors carry significant consequences. 07:17.630 --> 07:24.470 Examples include medical diagnosis support, financial modeling, compliance analysis, and legal document 07:24.470 --> 07:25.110 review. 07:25.710 --> 07:31.510 In these contexts, increased confidence and accuracy justify higher costs and latency. 07:32.110 --> 07:37.390 On the other hand, self-consistency should be avoided in real time chat applications. 07:37.590 --> 07:41.830 Simple information retrieval and high volume, low risk queries. 07:42.270 --> 07:48.710 In these cases, speed, responsiveness, and cost efficiency are far more important than marginal accuracy 07:48.710 --> 07:49.510 improvements. 07:50.070 --> 07:53.190 The core engineering principle is selectivity. 07:53.670 --> 07:59.140 Advanced techniques should be matched to task requirements not applied blindly. 07:59.740 --> 08:07.420 Overusing self-consistency can degrade system performance and inflate costs without meaningful benefits. 08:08.060 --> 08:12.100 The best systems use the right technique for the right problem. 08:12.100 --> 08:19.700 Role prompting assigns a specific identity or expertise to the model to guide tone, perspective, and 08:19.700 --> 08:20.460 relevance. 08:21.060 --> 08:27.060 Instead of asking the model to respond generically, you explicitly define who it should act as. 08:27.580 --> 08:35.340 For example, you are a senior data engineer or you are a compliance auditor reviewing this document. 08:35.940 --> 08:41.580 This technique reduces ambiguity and aligns responses with user expectations. 08:42.260 --> 08:49.020 A model instructed to act as a domain expert produces more focused, relevant, and appropriately detailed 08:49.020 --> 08:49.700 answers. 08:50.180 --> 08:56.330 Role prompting is especially effective for professional, technical, or regulatory tasks. 08:56.810 --> 09:02.450 Best practice is to place role definitions in the system prompt rather than the user prompt. 09:02.970 --> 09:09.690 This ensures consistency across interactions and prevents role drift during multi-turn conversations. 09:10.050 --> 09:16.210 Role prompting does not make the model truly knowledgeable in that role, but it strongly shapes how 09:16.250 --> 09:23.250 existing knowledge is expressed when combined with other techniques like chain of thought and constraints. 09:23.610 --> 09:30.090 Role prompting becomes a powerful tool for controlling AI behaviour in production systems. 09:31.210 --> 09:36.970 Constraints and guardrails are what turn prompts into reliable control systems. 09:37.370 --> 09:41.810 Without them, LLM outputs remain flexible but unpredictable. 09:42.330 --> 09:46.770 With them, behaviour becomes structured, consistent and safe. 09:47.570 --> 09:54.490 Format constraints specify exactly how outputs should be structured, such as JSON schemas, bullet 09:54.490 --> 09:59.970 lists, or tables, allowing downstream systems to parse responses reliably. 10:00.570 --> 10:07.010 Length and scope limits prevent runaway generation, and help maintain predictable performance and cost. 10:07.650 --> 10:11.610 Behavioral guardrails explicitly forbid unwanted actions. 10:12.130 --> 10:19.210 Instructions like do not make assumptions or, if unsure, respond with I don't know, dramatically 10:19.210 --> 10:21.730 reduce hallucinations and overconfidence. 10:22.210 --> 10:28.730 Safety and compliance constraints can enforce data handling rules, content policies, and regulatory 10:28.730 --> 10:31.930 requirements directly within the prompt architecture. 10:32.250 --> 10:36.530 The key takeaway is that constraints are not limitations. 10:36.930 --> 10:38.930 They are enablers of trust. 10:39.330 --> 10:46.690 Well-designed guardrails transform prompts from flexible text instructions into dependable, production 10:46.690 --> 10:48.410 ready control mechanisms. 10:48.970 --> 10:54.370 In serious AI systems, constraints are the foundation of reliability.